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Change in the inflammatory potential of diet over 10 years and subsequent mortality: the Multiethnic Cohort Study

Published online by Cambridge University Press:  08 April 2022

Song-Yi Park*
Affiliation:
Cancer Epidemiology Program, University of Hawaii Cancer Center, Honolulu, HI, USA
Chloe P. Lozano
Affiliation:
Cancer Epidemiology Program, University of Hawaii Cancer Center, Honolulu, HI, USA Ingestive Behavior, Weight Management & Health Promotion Laboratory, Pennington Biomedical Research Center, Baton Rouge, LA, USA
Yurii B. Shvetsov
Affiliation:
Cancer Epidemiology Program, University of Hawaii Cancer Center, Honolulu, HI, USA
Carol J. Boushey
Affiliation:
Cancer Epidemiology Program, University of Hawaii Cancer Center, Honolulu, HI, USA
Michael D. Wirth
Affiliation:
College of Nursing, University of South Carolina, Columbia, SC, USA Department of Epidemiology and Biostatistics, Arnold School of Public Health, University of South Carolina, Columbia, SC, USA Connecting Health Innovations LLC, Columbia, SC, USA
Nitin Shivappa
Affiliation:
Department of Epidemiology and Biostatistics, Arnold School of Public Health, University of South Carolina, Columbia, SC, USA Connecting Health Innovations LLC, Columbia, SC, USA
James R. Hébert
Affiliation:
Department of Epidemiology and Biostatistics, Arnold School of Public Health, University of South Carolina, Columbia, SC, USA Connecting Health Innovations LLC, Columbia, SC, USA
Christopher A. Haiman
Affiliation:
Department of Population and Public Health Sciences, Keck School of Medicine and Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, CA, USA
Lynne R. Wilkens
Affiliation:
Cancer Epidemiology Program, University of Hawaii Cancer Center, Honolulu, HI, USA
Loïc Le Marchand
Affiliation:
Cancer Epidemiology Program, University of Hawaii Cancer Center, Honolulu, HI, USA
*
*Corresponding author: Song-Yi Park, email spark@cc.hawaii.edu
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Abstract

Dietary inflammatory potential assessed by the Dietary Inflammatory Index (DII®) has been associated with health outcomes. However, longitudinal changes in the DII in relation to health outcomes rarely have been studied. This study aimed to examine change in the DII score over 10 years and its association with subsequent mortality in the Multiethnic Cohort. The analysis included 56 263 African American, Japanese American, Latino, Native Hawaiian and White participants who completed baseline (45–75 years) and 10-year follow-up surveys, including a FFQ. Mean energy-adjusted DII (E-DII) decreased over 10 years in men (from −0·85 to −1·61) and women (from −1·80 to −2·47), reflecting changes towards a more anti-inflammatory diet. During an average follow-up of 13·0 years, 16 363 deaths were identified. In multivariable Cox models, compared with anti-inflammatory stable individuals, risk of all-cause mortality was increased with pro-inflammatory change in men (hazard ratio (HR) = 1·13, 95 % CI 1·03, 1·23) and women (HR = 1·22, 95 % CI 1·13, 1·32). Per one-point increase in E-DII score over time, HR was 1·02 (95 % CI 1·00, 1·03) for men and 1·06 (95 % CI 1·04, 1·07) for women (P for heterogeneity < 0·001). While no heterogeneity by race and ethnicity was observed for men, the increased risk per one-point increase among women was stronger in non-Whites than in Whites (P for heterogeneity = 0·004). Our findings suggest that a change towards a more pro-inflammatory diet is associated with an increased risk of mortality both in men and women, and that the association is stronger in women, especially non-White women, than in men.

Type
Research Article
Copyright
© The Author(s), 2022. Published by Cambridge University Press on behalf of The Nutrition Society

Chronic inflammation plays a role in the development of non-communicable diseases, including CVD and cancer(Reference Phillips, Chen and Heude1). Diet is recognised as an important modulator of chronic inflammation(Reference Minihane, Vinoy and Russell2). Thus, pro- or anti-inflammatory properties of dietary components have been examined in relation to disease outcomes(Reference Kontogianni, Zampelas and Tsigos3,Reference Hardman4) . Given the complexity of the food combinations people consume, evaluating the overall inflammatory potential of the diet provides more intuitive results compared with individual dietary components in terms of disease and mortality risk prediction(Reference Phillips, Chen and Heude1). The Dietary Inflammatory Index (DII®) is a literature-derived diet quality score, developed to assess the inflammatory potential of an individual’s overall diet(Reference Shivappa, Steck and Hurley5). The evidence is growing that dietary inflammatory potential assessed by the DII is associated with health outcomes(Reference Ji, Hong and Chen6Reference Namazi, Larijani and Azadbakht12). However, longitudinal changes in the DII in relation to disease and mortality rarely have been studied, especially in racially/ethnically diverse populations. In a cohort of women with a majority of White participants, studies examining DII change over 3 years reported no association for overall breast cancer risk(Reference Tabung, Steck and Liese13) but an increased risk of proximal colon cancer with a pro-inflammatory change(Reference Tabung, Steck and Ma14).

Previously, we found in the Multiethnic Cohort (MEC) that a higher inflammatory potential of diet at cohort entry, assessed using the DII, was associated with an elevated risk of all-cause, CVD and cancer mortality overall and in most racial and ethnic groups(Reference Park, Kang and Wilkens15). In the current study, we examined change in DII scores after reassessing diet at 10 years from baseline and its association with subsequent all-cause, CVD and cancer mortality by sex and race/ethnicity in the MEC.

Methods

Study population

The MEC was designed to study lifestyle and genetic factors in relation to cancer and other chronic diseases(Reference Kolonel, Henderson and Hankin16). Between 1993 and 1996, more than 215 000 men and women aged 45–75 years and living in Hawaii or California were enrolled in MEC by completing a twenty-six-page mailed questionnaire on diet, medical history and lifestyle. Participants were mainly African American, Japanese American, Latino, Native Hawaiian or White and were recruited through targeted strategies. This study was conducted according to guidelines in the Declaration of Helsinki, and all procedures involving human subjects were approved by the Institutional Review Boards at the University of Hawaii and the University of Southern California. The Institutional Review Boards considered that informed consent was implied by the return of the baseline questionnaire that was mailed to potential participants along with a cover letter explaining the study. Between 2003 and 2008, all participants who were alive (∼89 %) were contacted for the 10-year follow-up and 98 214 of them repeated the comprehensive questionnaire. For the current analysis, we excluded participants who did not self-identify as one of the five major racial and ethnic groups (n 5265) or who reported implausible dietary data based on total energy intake or its components at cohort entry or follow-up survey (n 5928). Specifically, we calculated a robust standard deviation (sd) for the truncated normal distribution for the middle 80 % of the log energy distribution (after excluding the top and bottom 10 % tails). Then, we excluded all individuals with energy values beyond the ranges of the mean ± 3 robust SD. We applied similar approaches to exclude individuals with extreme fat, protein or carbohydrate intakes(Reference Nothlings, Wilkens and Murphy17). We further excluded participants who had prior heart disease or cancer at either survey (n 23 833) or had missing data on BMI or smoking at the follow-up survey (n 6925). Thus, data from a total of 56 263 men and women were included in this analysis. Online Supplementary Fig. S1 shows a flow diagram of study population and sample size.

Dietary assessment

Dietary intake was assessed at baseline and 10-year follow-up by a quantitative FFQ (QFFQ) as part of the comprehensive twenty-six-page questionnaire (available at https://www.uhcancercenter.org/mec). To estimate usual intake for over 180 food items during the past 12 months, the baseline QFFQ was developed from 3-d measured food records completed by approximately sixty men and women from each main racial/ethnic group(Reference Kolonel, Henderson and Hankin16). Daily intakes of foods and nutrients were calculated using a food composition table specific to the MEC. A calibration study within the MEC showed satisfactory Pearson Product Moment correlations for nutrients as energy densities (0·57–0·74) between the QFFQ and three 24-h recalls for all ethnic and sex groups being studied(Reference Stram, Hankin and Wilkens18). For the 10-year follow-up survey, the QFFQ was updated with new food products and written examples for each food item. In a second calibration study, correlations between the baseline and 10-year follow-up QFFQ were high for nutrient densities (0·70–0·74).

Dietary Inflammatory Index

The DII was developed and validated to determine an inflammatory effect score, based on nearly 2000 peer-reviewed articles published through 2010 on the association between diet and six inflammatory markers (i.e. C-reactive protein, IL-1β, IL-4, IL-6, IL-10 and TNF-α)(Reference Shivappa, Steck and Hurley5,Reference Shivappa, Steck and Hurley19) . A total of forty-five food components were identified as having sufficiently robust evidence linking them to at least one of the six markers. For the MEC, twenty-eight of the forty-five components were available for inclusion in the DII calculation: carbohydrate; protein; total fat; saturated, monounsaturated, and polyunsaturated fats; ω-3 and ω-6 FA; alcohol; fibre; cholesterol; vitamins A, B6, B12, C, D and E; thiamin; riboflavin; niacin; Fe; Mg; Zn; Se; folate; β-carotene; isoflavones; and caffeine(Reference Harmon, Wirth and Boushey20). Intake from foods only, not from supplements, was used in the DII calculation. The DII was standardised to its current range with the use of dietary intake from surveys or studies conducted in eleven countries. A z-score was created for each component for each participant and then converted to a centred proportion score. For these analyses, DII calculations are based on the energy density of each component (intake per 4184 kJ (1000 kcal)), also known as the energy-adjusted DII (E-DIITM)(Reference Harmon, Wirth and Boushey20). As for the DII, a higher E-DII score indicates a more pro-inflammatory diet, and a lower score indicates a more anti-inflammatory diet.

To determine the role of patterns of change in the inflammatory potential of diet over time in mortality risk, we applied Tabung et al.’s categorisation, first used in the Women’s Health Initiative(Reference Tabung, Steck and Liese13,Reference Tabung, Steck and Ma14) , to facilitate comparison of results across studies. We categorised E-DII scores at cohort entry and the 10-year follow-up surveys into sex-specific quintiles based on the distribution at cohort entry. We then further categorised change in E-DII scores between surveys based on change between sex-specific quintiles as follows:

  1. Anti-inflammatory stable: quintile 1 or 2 at both surveys, or change from quintile 3 to quintile 2.

  2. Anti-inflammatory change: downward change of at least 2 quintiles.

  3. Neutral inflammation stable: changes from quintile 2 to quintile 3 or from quintile 4 to quintile 3 or stable at quintile 3 at both surveys.

  4. Pro-inflammatory change: upward change of at least 2 quintiles.

  5. Pro-inflammatory stable: quintile 4 or quintile 5 at both surveys, or change from quintile 3 to quintile 4.

We also examined change in E-DII scores as a continuous variable, which was computed by subtracting E-DII at cohort entry from E-DII at 10-year follow-up.

Outcome ascertainment

Deaths among MEC participants were identified through linkage to death certificate files in Hawaii and California and the National Death Index through 31 December 2019. Causes of death were classified according to the International Classification of Diseases, 9th (ICD-9) and 10th revision (ICD-10) into CVD (ICD-9 codes 390–434, 436–448; ICD-10 codes I00-I78) and cancer (ICD-9 codes 140–208; ICD-10 codes C00-C97). During a mean follow-up of 13·0 (sd 3·5) years since the 10-year follow-up survey, a total of 16 363 deaths, including 3807 CVD and 3650 cancer deaths, were identified among the eligible participants.

Statistical analysis

A Cox proportional hazards model, with age as the time metric, was used to estimate hazard ratios (HR) and 95 % confidence intervals (CI) of mortality risk according to the E-DII change in men and women separately. For the E-DII change patterns, the anti-inflammatory stable group served as a reference category. The E-DII change also was modelled as a continuous variable to estimate HR of mortality per one-point increase in the E-DII score change. Basic models were adjusted for age at 10-year follow-up and race/ethnicity as covariates. The E-DII change was also fit as a continuous variable with adjustment for baseline E-DII score. For fully adjusted models, we used a comprehensive smoking model developed for lung cancer studies in the MEC(Reference Haiman, Stram and Wilkens21), because smoking is related to both diet quality and mortality outcome. The model included smoking status (never, former, current), average number of cigarettes, squared average number of cigarettes, number of years smoked (time dependent), number of years since quitting (time dependent) and interactions of race/ethnicity with smoking status, with average number of cigarettes, with squared average number of cigarettes and with number of years smoked. We further adjusted for BMI (< 25, 25–29·9, ≥ 30 kg/m2) and history of diabetes (yes, no) as strata variables, education (≤ 12, 13–15, ≥ 16 years, missing), marital status (married, not married, missing), moderate-to-vigorous physical activity (< 0·5, 0·5–< 1·3, ≥ 1·3 h/d, missing), alcohol intake (g/d), total energy intake (kcal/d) and menopausal hormone therapy use (never, ever, missing) for women only as covariates. The proportional hazards assumption was verified by Schoenfeld residuals(Reference Grambsch and Therneau22). We also ran the models for each race/ethnicity separately. Tests for heterogeneity by sex and race/ethnicity were based on Wald statistics for interaction terms of the E-DII change (continuous) and subgroup indicator. In sensitivity analyses, we removed deaths (n 910) that occurred within 2 years after the 10-year follow-up survey. We also examined a possible non-linear relationship between E-DII change and subsequent mortality non-parametrically with restricted cubic splines(Reference Durrleman and Simon23). All analyses were performed using SAS statistical software version 9.4 (SAS Institute Inc.).

Results

Over 10 years, mean E-DII scores decreased in both men (–0·85 to −1·61) and women (–1·80 to −2·47) and in all racial/ethnic groups within sexes reflecting changes towards a more anti-inflammatory diet (Table 1). Among men, Whites had the lowest E-DII scores at both the baseline and 10-year follow-up surveys (P < 0·001). In women, Japanese Americans had the lowest mean scores at both surveys (P < 0·001). Japanese Americans and Native Hawaiians showed the largest decrease over time in both men and women (P < 0·001).

Table 1. Energy-adjusted Dietary Inflammatory Index (E-DII) scores at cohort entry (1993–1996) and 10-year follow-up (2003–2008)

(Mean values and standard deviations)

* E-DII change = E-DII at 10-year follow-up – E-DII at cohort entry.

Compared with anti-inflammatory stable participants over the 10 years, pro-inflammatory stable individuals were more likely at baseline to be younger, Latino or Native Hawaiian, less educated, current smokers, be less physically active, have a higher BMI and to drink more alcoholic beverages. Results were consistent across both sexes, and women with higher E-DII scores were less likely to use menopausal hormone therapy (Table 2). Compared to participants with anti-inflammatory change in diet over time, those with pro-inflammatory change were more likely to be older, African American or White, less physically active and to drink more alcoholic beverages, findings that were consistent in both men and women.

Table 2. Characteristics of participants at 10-year follow-up (2003–2008) by E-DII change pattern*

(Mean values and standard deviations; numbers and percentages)

E-DII, energy-adjusted Dietary Inflammatory Index.

* See text for definition of the E-DII change categories.

Due to missing data, the percentages did not sum up to 100%.

Hours spent in moderate or vigorous activity.

In men, compared with anti-inflammatory stable individuals, those in all other groups showed a significantly higher risk of all-cause mortality, after adjustment for age and race/ethnicity (Table 3). After considering all potential confounders, the increased risk remained significant for the pro-inflammatory change (HR = 1·13, 95 % CI 1·03, 1·23) and pro-inflammatory stable (HR = 1·09, 95 % CI 1·02, 1·16) groups. Similarly, in women, pro-inflammatory change (HR = 1·22, 95 % CI 1·13, 1·32) and pro-inflammatory stable (HR = 1·12, 95 % CI 1·06, 1·20) groups had an increased risk in all-cause mortality in the fully adjusted model. Per one-point increase in E-DII scores over 10 years (change towards a more pro-inflammatory diet), the risk of all-cause mortality was higher in women (HR = 1·06, 95 % CI 1·04, 1·07) than in men (HR = 1·02, 95 % CI 1·00, 1·03, P for heterogeneity by sex < 0·001). This pattern also was observed for CVD mortality. Pro-inflammatory change in both men and women and being in the pro-inflammatory stable group in women were associated with an increased risk in CVD mortality in the fully adjusted model. The increase in risk in CVD mortality per one-point increase was higher in women than in men (P for heterogeneity by sex = 0·021). For cancer mortality, after adjusting for covariates, women in the pro-inflammatory stable group showed an increased risk. In the sensitivity analysis excluding deaths within the first 2 years of follow-up, the results remained similar. Based on non-parametric restricted cubic splines (online Supplementary Fig. S2), the relationship between E-DII change in score and subsequent mortality in men was linear for all-cause mortality (P for linearity = 0·013), while the non-linear components were not significant; there was no significant non-linear or linear relationship for CVD and cancer mortality. Among women, the relationship was non-linear for all-cause mortality (P for non-linearity = 0·005) with a J-shaped curve, while it was linear for CVD (P for linearity < 0·001) and cancer (P for linearity = 0·029) mortality (online Supplementary Fig. 2).

Table 3. E-DII change over 10 years and subsequent mortality from all causes, CVD and cancer, 2003–2019

(Hazard ratios and 95 % confidence intervals)

E-DII, energy-adjusted Dietary Inflammatory Index; HR, hazard ratio.

* Adjusted for age and race/ethnicity.

Further adjusted for BMI, history of diabetes, education, marital status, physical activity, alcohol intake, energy intake and menopausal hormone therapy use (for women only) in the smoking model, which included smoking status, average number of cigarettes, squared average number of cigarettes, number of years smoked (time dependent), number of years since quitting (time dependent) and interactions between race/ethnicity and smoking status, average number of cigarettes, squared average number of cigarettes and number of years smoked.

Based on per one-point increase in the multivariable-adjusted model.

§ Additionally adjusted for E-DII at cohort entry.

In race- and ethnicity-specific analysis among men, a statistically significant increase in all-cause mortality was observed in Whites (HR = 1·17, 95 % CI 1·00, 1·37) with pro-inflammatory change and in Latinos for the pro-inflammatory stable group (HR = 1·17, 95 % CI 1·02, 1·35) (Table 4). However, there was no indication of heterogeneity across the five groups (P = 0·65). In women, a significant increase in all-cause mortality was found among Japanese American, Latino and Native Hawaiian groups with pro-inflammatory change and Japanese American and White women who were pro-inflammatory stable. E-DII increase over time was associated with a higher risk of all-cause mortality in women more strongly among African American, Japanese American, Latino and Native Hawaiian women than among White women (P for heterogeneity = 0·014 across five racial and ethnic groups; 0·004 between non-White v. White groups).

Table 4. E-DII change over 10 years and subsequent all-cause mortality by race/ethnicity, 2003–2019

(Hazard ratios and 95 % confidence intervals)

E-DII, energy-adjusted Dietary Inflammatory Index; HR, hazard ratio.

* Adjusted for age, BMI, history of diabetes, education, marital status, physical activity, alcohol intake, energy intake and menopausal hormone therapy use (for women only) in the smoking model, which included smoking status, average number of cigarettes, squared average number of cigarettes, number of years smoked (time dependent) and number of years since quitting (time dependent).

Based on per one-point increase.

Additionally adjusted for E-DII at cohort entry.

§ P for heterogeneity between non-White v. White women = 0·004.

Discussion

In this multiethnic population, for both men and women, pro-inflammatory change in diet over 10 years was associated with an increased risk of subsequent mortality from all causes and CVD. Compared with men, the association with increasing E-DII score and all-cause mortality was stronger in women overall and in African American, Japanese American, Latino and Native Hawaiian, than White, women.

The inflammatory potential of the diet, as estimated by the E-DII, has been consistently associated with disease outcomes and mortality(Reference Ji, Hong and Chen6Reference Namazi, Larijani and Azadbakht12). However, there are only few studies that have contributed additional evidence by examining longitudinal changes in the inflammatory potential of diet(Reference Tabung, Steck and Liese13,Reference Tabung, Steck and Ma14,Reference Tabung, Steck and Zhang24Reference Dougherty, Lappe and Watson26) . In the Women’s Health Initiative Observational Study where FFQ were repeated among postmenopausal women (aged 50–79 years at baseline), mean E-DII score decreased modestly from –1·14 at baseline to –1·50 at Year 3 representing a transition towards an anti-inflammatory diet(Reference Tabung, Steck and Zhang24). In that cohort of women, of whom the majority were White, patterns of E-DII change over 3 years, as defined by Tabung et al. and described in the ‘Methods’ section, were not associated with risk of overall invasive breast cancer(Reference Tabung, Steck and Liese13). However, for ER–, PR– and HER2+ subtypes, the pro-inflammatory stable group showed an increase in risk compared with the anti-inflammatory stable group (HR = 1·85, 95 % CI 1·06, 3·13), suggesting that dietary inflammatory potential may differentially influence the development of breast cancer by phenotype(Reference Tabung, Steck and Liese13). In the same cohort, women with dietary changes towards, or a history of, pro-inflammatory diets had a higher risk of colon cancer compared with those in the anti-inflammatory stable group, particularly for proximal colon cancer (for pro-inflammatory change, HR = 1·32, 95 % CI 1·01, 1·74) and among non-users of non-steroidal anti-inflammatory drugs (for pro-inflammatory stable, HR = 1·42, 95 % CI 1·01, 2·03)(Reference Tabung, Steck and Ma14). Pro-inflammatory change in diet has been reported in a small cohort of Australian women (aged 51–62 years at baseline, –0·60 to –0·46 over 14 years)(Reference Hill, Hodge and Clifton25) and in rural postmenopausal women (55 years or older) in Nebraska over 4 years, which was larger in participants who developed cancer than in those without cancer(Reference Dougherty, Lappe and Watson26).

In the MEC, mean E-DII scores decreased over 10 years in both men (from −0·85 to −1·61) and women (from −1·80 to −2·47). Improvement in the inflammatory potential of the diet with age is consistent with previous research that found diet quality improves with age among MEC participants as assessed with the Healthy Eating Index-2015, the Alternative Healthy Eating Index-2010, the alternate Mediterranean diet score and the Dietary Approaches to Stop Hypertension score(Reference Park, Shvetsov and Kang27). These diet quality indexes were developed based on adherence to dietary recommendations or on their relation to reduced risk of chronic disease and mortality(Reference Liese, Krebs-Smith and Subar28), while the DII, and by logical extension the E-DII, was developed to characterise the inflammatory potential of the diet.

In the present study, the association between increase in dietary inflammation potential over time and subsequent all-cause and CVD mortality was stronger in women than in men. Women tended to have more anti-inflammatory diets at both cohort entry and 10-year follow-up and showed less changes in E-DII scores compared with men. A meta-analysis showed that increased risk of CVD incidence or mortality in individuals with the highest v. lowest DII score was significant in women but not in men(Reference Shivappa, Godos and Hebert8). However, within studies, no heterogeneity by sex was found for CVD mortality in the MEC(Reference Park, Kang and Wilkens15) and for CVD risk in other large US cohorts(Reference Li, Lee and Hu29). Men and women undergo different ageing-related changes including changes in body weight and composition and hormonal changes in sex hormones. However, in the present study, when we further adjusted for change in body weight over 10 years, the associations remained unchanged. Also, the associations were similar between menopausal hormone therapy ever users v. never users in women. Among MEC female participants, the association between increase in E-DII score and all-cause mortality was weaker in Whites, compared with other racial/ethnic groups. This may be due to White women having the lowest E-DII scores at cohort entry and one of the smallest changes in E-DII scores over time. Our research complements previous findings that the relationship between food consumption, diet quality and chronic inflammation may vary by sex and race/ethnicity(Reference Chai, Morimoto and Cooney30Reference Guillermo, Boushey and Franke33). However, potential differences in the E-DII change-mortality association by sex and race/ethnicity warrant further investigation.

This study has notable strengths including a population-based prospective design, a large sample size with participants from various racial/ethnic backgrounds, a validated FFQ and a wide range of covariates for diet-mortality analyses. However, dietary assessments based on a self-administered FFQ are subject to measurement error, which is most likely non-differential in a cohort study leading to attenuated risk estimates(Reference Freedman, Schatzkin and Midthune34). The sample size might be limited for some subgroup analyses. Despite the comprehensive information on lifestyle factors and careful adjustment for covariates, there is still the possibility of residual confounding by unmeasured or incompletely controlled variables that might be related to both diet and mortality. To compute the E-DII scores for the MEC, only twenty-eight of the forty-five components originally included for the DII development were available on our questionnaire. However, DII/E-DII scores based on fewer components have been demonstrated to adequately predict inflammatory markers and health outcomes in several studies(Reference Shivappa, Steck and Hurley19,Reference Shivappa, Hebert and Rietzschel35,Reference Tabung, Steck and Zhang36) including the previous reports from the MEC(Reference Park, Kang and Wilkens15,Reference Harmon, Wirth and Boushey20) . Because the current findings are from MEC participants who completed the 10-year follow-up survey (45 % of total) with further restriction to those (∼56 000) without prior heart disease or cancer, generalisability may be limited.

In conclusions, our findings suggest that pro-inflammatory change in diet in mid- to late adult life is associated with increased risk of mortality from all causes and CVD in both men and women, and that the association is stronger in women, especially non-White women, than in men.

Acknowledgements

The authors thank the MEC participants for their participation and commitment. This work was supported by the National Institutes of Health (grant numbers R03 CA223890, U01 CA164973).

Conceptualisation, S.-Y. P.; formal analysis, S.-Y. P. and L. R. W.; funding acquisition S.-Y. P., C. A. H., L. R. W. and L. L. M.; investigation, S.-Y. P.; methodology, S.-Y. P., L. R. W., M. D. W., N. S. and J. R. H.; supervision, L. L. M.; writing – original draft, S.-Y. P. and L. L. M.; writing – review and editing, S.-Y. P., C. P. L., Y. B. S., C. J. B., M. D. W., N. S., J. R. H., C. A. H., L. R. W. and L. L. M.

All authors declare no conflict of interest. We wish to disclose that Dr. Hébert owns controlling interest in Connecting Health Innovations LLC (CHI), a company that has licensed the right to his invention of the Dietary Inflammatory Index™ (DII®) from the University of South Carolina in order to develop computer and smartphone applications for patient counselling and dietary intervention in clinical settings. Drs. Wirth and Shivappa are employees of CHI. The subject matter of this paper will not have any direct bearing on that work, nor has that activity exerted any influence on this project.

Supplementary material

For supplementary material referred to in this article, please visit https://doi.org/10.1017/S0007114522000861

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Figure 0

Table 1. Energy-adjusted Dietary Inflammatory Index (E-DII) scores at cohort entry (1993–1996) and 10-year follow-up (2003–2008)(Mean values and standard deviations)

Figure 1

Table 2. Characteristics of participants at 10-year follow-up (2003–2008) by E-DII change pattern*(Mean values and standard deviations; numbers and percentages)

Figure 2

Table 3. E-DII change over 10 years and subsequent mortality from all causes, CVD and cancer, 2003–2019(Hazard ratios and 95 % confidence intervals)

Figure 3

Table 4. E-DII change over 10 years and subsequent all-cause mortality by race/ethnicity, 2003–2019(Hazard ratios and 95 % confidence intervals)

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