Background: Inflammation is important in chronic disease and can be modulated by dietary exposures. Our aim was to examine whether the inflammatory potential of diet after cancer diagnosis, assessed using the dietary inflammatory index (DII), is associated with all-cause and cause-specific mortality among women diagnosed with invasive breast cancer in the Women's Health Initiative (WHI).

Methods: Our analytic cohort included 2,150 postmenopausal women, ages 50 to 79 years at baseline, who developed invasive breast cancer during follow-up and completed a food frequency questionnaire (FFQ) on average 1.5 years after diagnosis. Women were followed from breast cancer diagnosis until death or the end of follow-up by October 2014. Energy-adjusted DII (E-DII) scores were calculated from food plus supplements using a nutrient–density approach. Cox proportional hazards models were fit to estimate multivariable-adjusted HRs and 95% confidence intervals (CIs) for all-cause, breast cancer–specific, and cardiovascular disease (CVD) mortality.

Results: After a median 13.3 years of follow-up, 580 deaths from any cause occurred, including 212 breast cancer deaths and 103 CVD deaths. Lower (i.e., more anti-inflammatory) E-DII scores were associated with a lower risk of CVD mortality (HRQ1VSQ4 = 0.44; 95% CI, 0.24–0.82; Ptrend = 0.005), but not with breast cancer–specific mortality (HRQ1VSQ4 = 0.96; 95% CI, 0.62–1.49; Ptrend = 0.96) or all-cause mortality (HRQ1VSQ4 = 0.82; 95% CI, 0.63–1.05; Ptrend = 0.17).

Conclusions: Consuming a more anti-inflammatory diet after breast cancer diagnosis may be a means for reducing risk of death from CVD.

Impact: Survival after invasive breast cancer diagnosis may be improved by consumption of an anti-inflammatory diet. Cancer Epidemiol Biomarkers Prev; 27(4); 454–63. ©2018 AACR.

Breast cancer is the most frequently diagnosed cancer among women in the United States and ranks second after lung cancer as a cause of cancer-related death (1). It is estimated that more than 4.5 million female invasive breast cancer survivors will be alive in the United States by 2026, which constitutes the largest cancer survivor group (2). As breast cancer survival rates are relatively high (5-year survival rates are 89% for all stages combined) and have been increasing in recent years due to widespread use of mammography and improvements in treatment (2), breast cancer survivors experience increased risks of postdiagnostic comorbidities, such as hypertension, arthritis, chronic pulmonary disease, cardiovascular disease (CVD), and diabetes, which have a significant impact on their overall survival (3–5). Therefore, implementing healthy lifestyle changes, including dietary improvement after cancer diagnosis has the potential to exert a strong influence on breast cancer survival. Research has suggested that a majority of breast cancer survivors are highly motivated to make changes in their diets and supplement use after cancer diagnosis (6, 7).

To inform postdiagnosis dietary recommendations, eight observational studies examined associations between postdiagnostic diet quality as assessed using different dietary patterns and breast cancer survival among women diagnosed with invasive breast cancer. Among these studies, three assessed diet quality from a posteriori (i.e., data-driven) dietary patterns (8–10), and five focused on a priori (score-based) dietary patterns after a breast cancer diagnosis (11–15), with most finding no association with breast cancer–specific mortality but inverse associations between diet quality and non–breast cancer mortality. In addition, three randomized dietary intervention trials assessed whether healthy dietary intervention among women with breast cancer could improve their breast cancer prognosis and overall survival, but reported inconsistent findings (16–18). One dietary intervention to reduce fat intake observed significantly improved relapse-free survival of breast cancer (16); postmenopausal breast cancer survivors in the Women's Health Initiative Dietary Modification Trial (WHI-DM) with a low-fat dietary pattern had a significant reduced risk of death after breast cancer compared with the usual diet comparison group (18), wherease in the third study, adoption of a diet that was very high in vegetables, fruit, and fiber and low in fat did not reduce additional breast cancer events or all-cause mortality (17).

Some dietary indices are limited by the relatively small numbers of dietary components included and the lack of focus on specific biologic pathways related to chronic disease and mortality. Given the important role of inflammation in the pathogenesis of many chronic conditions and outcomes, such as cancer and CVD incidence and death (19–23), a dietary index that focuses on the inflammatory potential of diet as a whole may be better able to predict mortality among cancer patients compared with others focused solely on specific food items derived from data-driven methods or more general dietary guidelines. Therefore, we undertook an analysis to assess the inflammatory potential of postdiagnosis diet using the literature-derived dietary inflammatory index (DII®; ref. 24) and examined its association with all-cause mortality, breast cancer–specific mortality, and CVD mortality among women who were diagnosed postmenopause with invasive breast cancer from the WHI Study.

Study population

The WHI was established to explore some of the most common predictors of morbidity and mortality among women who were postmenopause, including cancer, CVD, and osteoporotic fractures. Details of the design of the WHI have been described previously (25–27). Briefly, 161,808 women who were postmenopause aged 50 to 79 years were enrolled between 1993 and 1998 from 40 WHI clinical centers across the United States into either one or multiple of three randomized controlled Clinical Trials (CT; n = 68,132), which consisted of hormone replacement therapy (HRT) trial, dietary modification (DM) trial, and calcium and vitamin D supplement (CaD) trial or the Observational Study (OS; n = 93,676). Women were not eligible for either the CT or the OS if they had any medical condition with predicted survival of less than 3 years, or had active participation in other randomized intervention trials. For the WHI-DM trial, participants were additionally excluded if their diet had less than 32% energy from fat or they were on a diabetic or low-salt diet (26). The primary follow-up of CT and OS was closed in 2005, and the participants who consented were continuously followed up in the WHI Extension Study I (2005–2010) and II (2010–2015). Participants from the WHI-DM and WHI-OS had repeated FFQs during follow-up, which allowed us to assess the post-cancer diagnosis diet. We excluded women diagnosed with breast cancer who were enrolled in the WHI-CaD or the WHI-HRT (n = 2,527) because they did not complete follow-up FFQs. We also excluded 2,968 WHI-OS participants who did not complete a FFQ after breast cancer diagnosis, and WHI-DM participants who did not complete a FFQ after breast cancer diagnosis but prior to death. Thus, our study included women from WHI-DM and WHI-OS, who were free of cancer at or before baseline except nonmelanoma skin cancer, were diagnosed with invasive breast cancer as a first primary cancer during follow-up, and completed an FFQ after diagnosis (n = 2,242; WHI-OS = 1,008 and WHI-DM = 1,234). Among these, we excluded 86 women (WHI-OS = 37; WHI-DM = 49) who reported implausible daily energy intake (outside the range of 600–5,000 kcals/day), 4 women who did not contribute follow-up time in the cohort, and 2 women who did not have data on HRT. A total of 2,150 women were included in the analysis. The WHI protocol was approved by the Institutional Review Boards at the Clinical Coordinating Center (CCC) at the Fred Hutchinson Cancer Research Center (Seattle, WA) and at each of the participating Clinical Centers. All participants provided written informed consent in accordance with the U.S. Common Rule.

Dietary assessment

Diet in the past 3 months was measured using a self-administered FFQ, adapted from instruments previously used in large-scale dietary intervention studies (28–31). The FFQ consisted of three sections: (i) 120 food or food groups with questions on frequency of intake and portion size; (ii) four summary questions related to the usual intake of fruits, vegetables, and added fat to compare with information gathered from the food items; and (iii) 19 adjustment questions that solicited information on food preparation methods and types of fat added so as to permit more refined calculation of fat intake. For quality control purposes, all adjustment and summary questions, 90% of the foods, and at least one-half of every food group section had to be completed (32). Nutrient intake from the FFQ was calculated by linking to the University of Minnesota Nutrition Coordinating Center food and nutrient database (33). Compared with four 24-hour dietary recalls and a 4-day food record within the WHI study, the energy-adjusted correlation coefficient for dietary factors in the WHI FFQ ranged from 0.18 for vitamin B12 to 0.68 for magnesium, with a mean of 0.49 (32).

WHI-OS participants completed the FFQ at baseline and year 3 of follow-up, and WHI-DM participants completed the FFQ at baseline and year 1 of follow-up, and thereafter, a random subset of approximately one third of DM participants completed the FFQ each year from year 2 to year 9 (34). For the current analysis, we chose the first FFQ that occurred after participants' diagnoses of invasive breast cancer. The identified FFQ occurred, on average, 1.55 years after breast cancer diagnosis for WHI-DM and 1.48 years for OS participants. Data on dietary supplement use were retrieved from baseline and annual visits for WHI-CT and from year 3 follow-up visits for OS where participants brought in their medications and dietary supplements in their original pill bottles. Similar to the identification of post-cancer diagnosis FFQ, we chose supplement use information reported soonest after participants' diagnoses of invasive breast cancer.

Description of energy-adjusted DII score

The energy-adjusted DII (E-DII) score for each participant was calculated on the basis of the nutrient and food intake derived from the WHI FFQ with linkage to the literature-derived inflammatory effect scores included in the DII, which was developed at the University of South Carolina (Columbia, SC; ref. 24). The details of the development and construct validation of DII have been published previously (24, 35–39). Briefly, a total of 1,943 qualifying peer-reviewed primary research articles published through 2010 on the effect of dietary factors on six inflammatory markers (IL1β, IL4, IL6, IL10, TNFα, and C-reactive protein) were identified and scored to derive the component-specific inflammatory effect scores for 45 dietary factors (i.e., components of DII), comprising macronutrients and micronutrients as well as some bioactive components (24). Thirteen DII components, including ginger, turmeric, garlic, oregano, pepper, rosemary, eugenol, saffron, flavan-3-ol, flavones, flavonols, flavonones, and anthocyanidins, were not available from the WHI FFQ; therefore, we used the 32 components available in the WHI FFQ to calculate the E-DII score for our analysis. The majority (75%) of participants took supplements after their breast cancer diagnoses, and most nutrients contained in dietary supplements have anti-inflammatory properties (24). In addition, findings from a WHI study reported that multivitamin use was associated with lower breast cancer mortality among women who were postmenopause and who had invasive breast cancer (40). Therefore, in our study, we calculated E-DII score from foods plus supplements as the exposure to reflect overall post-cancer dietary quality with regard to inflammatory potential and examined its association with mortality outcomes. In a DII construct validation study using data from the WHI, the DII score calculated on the basis of food and supplement intake of these 32 components was associated with concentrations of inflammatory markers, including IL6 and TNFα receptor 2 (36).

The WHI FFQ-derived food and nutrient consumption was adjusted first for total energy per 1,000 calories and standardized to an energy-adjusted worldwide representative diet database, which included dietary data from 11 populations across the world to avoid the arbitrary use of raw consumption amounts (24, 41). The energy-adjusted standardized dietary intake was then multiplied by the literature-derived inflammatory effect score for each DII component and summed across all components to obtain the overall energy-adjusted E-DII score (24). Higher E-DII scores represent more proinflammatory diets, whereas lower (i.e., more negative) E-DII scores indicate more anti-inflammatory diets.

Other covariate assessments

Information on age at screening, race/ethnicity, education level, family income level, and hormone use was assessed at baseline using self-administered questionnaires. Energy expenditure from recreational physical activity in MET-hours/week (includes walking, mild, moderate, and strenuous physical activity) and smoking status were assessed as part of personal habits using the self-administered questionnaires at baseline for all WHI-OS and WHI-DM participants and updated for the WHI-DM only at years 1, 3, 6, and 9. We chose physical activity and smoking status assessed at baseline in our analysis to ensure consistency in the timing of assessments for the entire study population. Physical activity was categorized to four levels (0 MET-hours/week; 0.1–3 MET-hours/week; 3.1–8.9 MET-hours/week; 9 or more MET-hours/week; refs. 11, 42). Weight and height were measured using standard methods during clinic visits at baseline and annually from years 1 to 9 for the WHI-DM and at baseline and year 3 for the WHI-OS. Baseline, instead of post-cancer diagnosis, weight, and height were used to calculate the body mass index (BMI) as weight (kg)/height (m)2 due to considerable missing data on post-cancer diagnosis information for OS. We categorized the baseline BMI based on the World Health Organization criteria (43).

Identification and ascertainment of incident breast cancer cases have been described in detail elsewhere (44). Briefly, medical records from participants who self-reported outcomes were reviewed first by local physician adjudicators to assign a diagnosis. Centralized review and coding based on related diagnostic documents were subsequently performed at the Clinical Coordinating Center. Detailed cancer characteristics, such as stage, anatomic subsite, diagnosis date, extent of disease (stage, tumor size, laterality), tumor morphology (behavior, grade, histology), and hormone receptor results, were recorded using the Surveillance, Epidemiology, and End Results (SEER) coding guidelines (45).

Ascertainment of death

There are three mortality outcomes in our analysis: death from any cause, death from breast cancer, and death from CVD. CVD deaths in WHI included deaths from definite coronary heart disease (CHD), cerebrovascular diseases, pulmonary embolism, possible CHD, other CVD, and unknown CVD based on ICD-9 codes 390–459 or ICD-10 codes I00–I99. Vital status of participants was updated by contacts during annual clinic visits for CT and through mailings for the OS (25). Autopsy and hospitalization records were used to determine the underlying cause of death. If these were unavailable, death certificates, medical records, or other records were used (44). In addition, data linkage with the National Death Index was performed periodically for all WHI participants to identify otherwise unreported deaths and to confirm causes of death (44).

Statistical analysis

To describe demographic, lifestyle, and clinical characteristics of our study population, we calculated means and SEs for continuous variables and number and frequencies for categorical variables by quartiles of E-DII scores. ANOVA was used to test for differences in continuous variables across E-DII quartiles if variance across E-DII quartiles was homogeneous. Welch test was used if variance was heterogeneous (46). The χ2 test was performed to test for differences in categorical variables.

For each mortality outcome, women were followed from diagnosis of primary invasive breast cancer until death, loss to follow-up, the last NDI search date for the participant, or the end of follow-up by October 2014. Cox proportional hazards models, with person-years as the underlying time metric, were fitted to estimate age and energy-adjusted and multivariable-adjusted HRs and 95% confidence intervals (CI) with women in the highest E-DII quartile (most proinflammatory scores) as the referent. To account for the time period from breast cancer diagnosis to FFQ completion when no subjects were at risk of death due to our study design, we added a time-dependent covariate in the model to stratify participants' status before and after the postdiagnosis FFQ. The proportional hazard (PH) assumption was examined using the Schoenfeld residual test (47). There was no evidence that the E-DII violated PH assumption. However, in analyses where covariates violated the PH assumption, we fitted an extended Cox proportional hazards model (i.e., stratified by categorical covariate or added a time-dependent covariate, which was formed by the product of time and continuous covariate; refs. 48, 49). To test for linear trend in mortality risk across E-DII scores, a continuous E-DII score was used. In multivariable-adjusted models, we adjusted for WHI study component (WHI-OS, WHI-DM-intervention, WHI-DM-control), family income levels, age group at cancer diagnosis, race/ethnicity, education level, cancer stage, years from cancer diagnosis to FFQ completion, baseline physical activity level, smoking status at baseline, baseline BMI categories, total energy intake per day, estrogen receptor (ER) status, progesterone receptor (PR) status, and HRT usage. Although none of these covariates changed the crude HR of all-cause mortality by more than 10%, we maintained them in the model because they were considered to be important predictors of survival after breast cancer diagnosis in the WHI (11). Cancer stage and ER/PR status were used as proxy for the currently unavailable cancer treatment data, as breast cancer stage and hormone receptor status may influence types of treatment received (50–52).

Breast cancer hormone receptor status is an important predictor of prognosis, and diet may have differential effects on breast cancer development and overall survival depending on hormone receptor status (53, 54). Thus, we planned a priori stratified analysis by ER, PR, and combined ER and PR status on the association between E-DII and all-cause mortality. Given the small number of breast cancer–specific deaths in this study, we did not further stratify analyses of breast cancer–specific mortality by hormone receptor status. Cross products of categorical E-DII and ER status and E-DII and PR status were added into the multivariable-adjusted Cox proportional hazards model separately, and the likelihood ratio tests were used to evaluate effect modification. P ≤ 0.10 indicated significant interaction.

In sensitivity analysis, we excluded participants from the WHI-DM-intervention arm because long-term dietary intervention can change an individual's dietary habits. We also restricted our analysis to subjects with an FFQ completed at least 1 year after their cancer diagnosis because cancer treatment may affect the diets of patients with cancer in the first year. Women excluded from our analyses due to death before they could complete a postdiagnosis FFQ were more likely to consume proinflammatory diets than our analytic sample. To evaluate the potential for selection bias, demographic and lifestyle factors and tumor characteristics were compared between our sample and all breast cancer survivors in the WHI using either an independent t test or a χ2 test to assess the difference for continuous variables and categorical variables, respectively.

All statistical analyses were conducted using SAS (version 9.4). All tests were two-sided with P values <0.05 considered to be statistically significant if not otherwise noted.

After a median 13.3 years of follow-up, 580 deaths occurred, including 212 breast cancer deaths and 103 CVD deaths. The other main causes of death included lung cancer, pneumonia, other causes, or unknown causes. As shown in Table 1, compared with women with the most proinflammatory diets (i.e., E-DII quartile 4), women consuming more anti-inflammatory diets had lower daily energy intake, higher income and education level, longer survival after cancer diagnoses, were more physically active, had lower BMI at baseline, and were more likely to be enrolled in the WHI-DM-intervention arm, be white/non-Hispanic, or be never or past smokers.

Table 1.

Demographic, lifestyle, and clinical characteristics of 2,150 postmenopausal women diagnosed with invasive breast cancer in the WHI-DM and WHI-OS by quartile of E-DII

Most anti-inflammatory dietMost proinflammatory diet
E-DII quartile 1 (−6.81, −4.49)E-DII quartile 2 (−4.48, −3.46)E-DII quartile 3 (−3.45, −2.01)E-DII quartile 4 (−2.00, 3.79)Pa
Number of subjects 538 537 538 537  
 Mean (SE) Mean (SE) Mean (SE) Mean (SE)  
Age at breast cancer diagnosis (years) 65.6 (0.3) 66.4 (0.3) 66.6 (0.3) 66.3 (0.3) 0.08 
Years from breast cancer diagnosis to FFQ 1.5 (0.05) 1.5 (0.05) 1.6 (0.05) 1.5 (0.05) 0.57 
Years from breast cancer diagnosis to death from any cause 8.4 (0.3) 8.1 (0.3) 7.5 (0.3) 7.3 (0.3) 0.03 
Total energy intake after cancer diagnosis (kcal/day) 1,366.7 (18.5) 1,506.2 (20.5) 1,631.1 (25.7) 1,665.4 (28.4) <0.0001 
BMI at enrollment (kg/m227.0 (0.3) 27.7 (0.3) 28.5 (0.2) 29.6 (0.3) <0.0001 
Physical activity at enrollment in MET- hours/week 15.5 (0.7) 13.2 (0.6) 10.4 (0.5) 8.7 (0.5) <0.0001 
 n (%)b n (%)b n (%)b n (%)b  
WHI components     <0.0001 
 WHI OS 285 (53.0) 258 (48.0) 220 (40.9) 205 (38.2)  
 WHI-DM intervention 134 (24.9) 122 (22.8) 116 (21.6) 76 (14.1)  
 WHI-DM control 119 (22.1) 157 (29.2) 202 (37.5) 256 (47.7)  
Race/ethnicity     <0.0001 
 White non-Hispanic 482 (89.6) 498 (92.7) 475 (88.3) 448 (83.4)  
 Hispanic/Latino 11 (2.0) 11 (2.1) 15 (2.8) 16 (3.0)  
 Black/African American 19 (3.5) 17 (3.2) 26 (4.8) 56 (10.4)  
 Other 26 (4.8) 11 (2.0) 22 (4.1) 17 (3.2)  
Income level     <0.0001 
 <20,000 45 (8.4) 45 (8.4) 76 (14.1) 89 (16.6)  
 20,000–49,999 209 (38.8) 252 (46.9) 215 (40.0) 241 (44.9)  
 ≥50,000 257 (47.8) 216 (40.2) 218 (40.5) 170 (31.7)  
 Missing 27 (5.0) 24 (4.5) 29 (5.4) 37 (6.9)  
Education level     <0.0001 
 High school or below 105 (19.5) 120 (22.3) 140 (26.0) 178 (33.0)  
 Some college 144 (26.8) 149 (27.8) 159 (29.5) 167 (31.0)  
 College 72 (13.4) 77 (14.3) 74 (13.7) 51 (9.5)  
 Post graduate 213 (39.6) 187 (34.8) 163 (30.2) 137 (25.4)  
 Missing 4 (0.7) 4 (0.7) 3 (0.6) 6 (1.1)  
Cancer stage     0.36 
 Localized 416 (77.3) 403 (75.0) 394 (73.2) 393 (73.2)  
 Regional 114 (21.2) 127 (23.7) 130 (24.2) 137 (25.5)  
 Distant 4 (0.7) 4 (0.7) 4 (0.7) 4 (0.7)  
 Unknown 4 (0.7) 3 (0.6) 10 (1.9) 3 (0.6)  
Smoking status at enrollment     0.02 
 Never smoked 228 (42.4) 281 (52.3) 266 (49.4) 261 (48.6)  
 Past smoker 269 (50.0) 230 (42.8) 239 (44.4) 229 (42.6)  
 Current smoker 28 (5.2) 20 (3.7) 25 (4.7) 39 (7.3)  
 Missing 13 (2.4) 6 (1.1) 8 (1.5) 8 (1.5)  
ER status     0.12 
 Positive 429 (79.7) 400 (74.5) 411 (76.4) 391 (72.8)  
 Negative 63 (11.7) 74 (13.8) 71 (13.2) 91 (17.0)  
 Othersc 46 (8.6) 63 (11.7) 56 (10.4) 55 (10.2)  
PR status     0.21 
 Positive 351 (65.2) 321 (59.8) 350 (65.1) 317 (59.0)  
 Negative 125 (23.2) 143 (26.6) 125 (23.2) 153 (28.5)  
 Othersc 62 (11.5) 73 (13.6) 63 (11.7) 67 (12.5)  
Most anti-inflammatory dietMost proinflammatory diet
E-DII quartile 1 (−6.81, −4.49)E-DII quartile 2 (−4.48, −3.46)E-DII quartile 3 (−3.45, −2.01)E-DII quartile 4 (−2.00, 3.79)Pa
Number of subjects 538 537 538 537  
 Mean (SE) Mean (SE) Mean (SE) Mean (SE)  
Age at breast cancer diagnosis (years) 65.6 (0.3) 66.4 (0.3) 66.6 (0.3) 66.3 (0.3) 0.08 
Years from breast cancer diagnosis to FFQ 1.5 (0.05) 1.5 (0.05) 1.6 (0.05) 1.5 (0.05) 0.57 
Years from breast cancer diagnosis to death from any cause 8.4 (0.3) 8.1 (0.3) 7.5 (0.3) 7.3 (0.3) 0.03 
Total energy intake after cancer diagnosis (kcal/day) 1,366.7 (18.5) 1,506.2 (20.5) 1,631.1 (25.7) 1,665.4 (28.4) <0.0001 
BMI at enrollment (kg/m227.0 (0.3) 27.7 (0.3) 28.5 (0.2) 29.6 (0.3) <0.0001 
Physical activity at enrollment in MET- hours/week 15.5 (0.7) 13.2 (0.6) 10.4 (0.5) 8.7 (0.5) <0.0001 
 n (%)b n (%)b n (%)b n (%)b  
WHI components     <0.0001 
 WHI OS 285 (53.0) 258 (48.0) 220 (40.9) 205 (38.2)  
 WHI-DM intervention 134 (24.9) 122 (22.8) 116 (21.6) 76 (14.1)  
 WHI-DM control 119 (22.1) 157 (29.2) 202 (37.5) 256 (47.7)  
Race/ethnicity     <0.0001 
 White non-Hispanic 482 (89.6) 498 (92.7) 475 (88.3) 448 (83.4)  
 Hispanic/Latino 11 (2.0) 11 (2.1) 15 (2.8) 16 (3.0)  
 Black/African American 19 (3.5) 17 (3.2) 26 (4.8) 56 (10.4)  
 Other 26 (4.8) 11 (2.0) 22 (4.1) 17 (3.2)  
Income level     <0.0001 
 <20,000 45 (8.4) 45 (8.4) 76 (14.1) 89 (16.6)  
 20,000–49,999 209 (38.8) 252 (46.9) 215 (40.0) 241 (44.9)  
 ≥50,000 257 (47.8) 216 (40.2) 218 (40.5) 170 (31.7)  
 Missing 27 (5.0) 24 (4.5) 29 (5.4) 37 (6.9)  
Education level     <0.0001 
 High school or below 105 (19.5) 120 (22.3) 140 (26.0) 178 (33.0)  
 Some college 144 (26.8) 149 (27.8) 159 (29.5) 167 (31.0)  
 College 72 (13.4) 77 (14.3) 74 (13.7) 51 (9.5)  
 Post graduate 213 (39.6) 187 (34.8) 163 (30.2) 137 (25.4)  
 Missing 4 (0.7) 4 (0.7) 3 (0.6) 6 (1.1)  
Cancer stage     0.36 
 Localized 416 (77.3) 403 (75.0) 394 (73.2) 393 (73.2)  
 Regional 114 (21.2) 127 (23.7) 130 (24.2) 137 (25.5)  
 Distant 4 (0.7) 4 (0.7) 4 (0.7) 4 (0.7)  
 Unknown 4 (0.7) 3 (0.6) 10 (1.9) 3 (0.6)  
Smoking status at enrollment     0.02 
 Never smoked 228 (42.4) 281 (52.3) 266 (49.4) 261 (48.6)  
 Past smoker 269 (50.0) 230 (42.8) 239 (44.4) 229 (42.6)  
 Current smoker 28 (5.2) 20 (3.7) 25 (4.7) 39 (7.3)  
 Missing 13 (2.4) 6 (1.1) 8 (1.5) 8 (1.5)  
ER status     0.12 
 Positive 429 (79.7) 400 (74.5) 411 (76.4) 391 (72.8)  
 Negative 63 (11.7) 74 (13.8) 71 (13.2) 91 (17.0)  
 Othersc 46 (8.6) 63 (11.7) 56 (10.4) 55 (10.2)  
PR status     0.21 
 Positive 351 (65.2) 321 (59.8) 350 (65.1) 317 (59.0)  
 Negative 125 (23.2) 143 (26.6) 125 (23.2) 153 (28.5)  
 Othersc 62 (11.5) 73 (13.6) 63 (11.7) 67 (12.5)  

aP value was calculated from ANOVA test for continuous variables and from χ2 test for categorical variables.

bThe sum of percentages in certain E-DII quartile for some categorical variables may not add up to 100% because of rounding.

cThis category included data from borderline status or missing.

Women consuming the most anti-inflammatory diets compared with the most proinflammatory diets had a 56% lower risk of death from CVD based on multivariable Cox proportional hazards model (HRQ1VSQ4 = 0.44; 95% CI, 0.24–0.82; Table 2). However, no association was observed for E-DII with all-cause mortality (HRQ1VSQ4 = 0.82; 95% CI, 0.63–1.05) or with breast cancer–specific mortality (HRQ1VSQ4 = 0.96; 95% CI, 0.62–1.49; Table 2). When we excluded women from the WHI-DM-intervention (n = 448), the HRs did not change materially (Supplementary Table S1). After we excluded women with postdiagnosis FFQs completed within 1 year after their diagnoses (n = 865), the HRs were similar for all-cause mortality and breast cancer–specific mortality, but the protective effect of an anti-inflammatory diet on CVD mortality was attenuated (HRQ1VSQ4 = 0.69; 95% CI, 0.30–1.60; Supplementary Table S2), which may be largely due to the small sample size and small number of deaths from CVD in this subsample. Compared with our smaller subset, total breast cancer cases in the WHI were, on average, older at diagnosis and had shorter survival, but major differences in risk factors related to mortality or a proinflammatory diet were not observed between these two groups (Supplementary Table S3).

Table 2.

Association of postdiagnosis E-DII with all-cause mortality, breast cancer–specific mortality, and CVD mortality among 2,150 women diagnosed with invasive breast cancer in the WHI-DM and OS

Most anti-inflammatory dietMost pro-inflammatory diet
E-DII score quartile 1 (−6.81, −4.49)E-DII score quartile 2 (−4.48, −3.46)E-DII score quartile 3 (−3.45, −2.01)E-DII score quartile 4 (−2.00, 3.79)Ptrenda
N 538 537 538 537  
Death from any cause (n130 148 141 161  
 Age- and energy-adjusted HR (95% CI) 0.67 (0.53–0.85) 0.81 (0.65–1.01) 0.77 (0.62–0.97) 1.00 (referent) 0.0003 
 Multivariable-adjusted HR (95% CI)b 0.82 (0.63–1.05) 0.96 (0.76–1.22) 0.86 (0.68–1.08) 1.00 (referent) 0.17 
Death from breast cancer (n46 58 57 51  
 Age- and energy-adjusted HR (95% CI) 0.81 (0.54–1.22) 1.06 (0.73–1.55) 1.09 (0.74–1.59) 1.00 (referent) 0.43 
 Multivariable-adjusted HR (95% CI)c 0.96 (0.62–1.49) 1.20 (0.80–1.80) 1.18 (0.80–1.76) 1.00 (referent) 0.96 
Death from CVD (n18 26 26 33  
 Age- and energy-adjusted HR (95% CI) 0.44 (0.24–0.78) 0.67 (0.40–1.12) 0.65 (0.39–1.08) 1.00 (referent) 0.002 
 Multivariable-adjusted HR (95% CI)d 0.44 (0.24–0.82) 0.69 (0.40–1.20) 0.66 (0.38–1.12) 1.00 (referent) 0.005 
Most anti-inflammatory dietMost pro-inflammatory diet
E-DII score quartile 1 (−6.81, −4.49)E-DII score quartile 2 (−4.48, −3.46)E-DII score quartile 3 (−3.45, −2.01)E-DII score quartile 4 (−2.00, 3.79)Ptrenda
N 538 537 538 537  
Death from any cause (n130 148 141 161  
 Age- and energy-adjusted HR (95% CI) 0.67 (0.53–0.85) 0.81 (0.65–1.01) 0.77 (0.62–0.97) 1.00 (referent) 0.0003 
 Multivariable-adjusted HR (95% CI)b 0.82 (0.63–1.05) 0.96 (0.76–1.22) 0.86 (0.68–1.08) 1.00 (referent) 0.17 
Death from breast cancer (n46 58 57 51  
 Age- and energy-adjusted HR (95% CI) 0.81 (0.54–1.22) 1.06 (0.73–1.55) 1.09 (0.74–1.59) 1.00 (referent) 0.43 
 Multivariable-adjusted HR (95% CI)c 0.96 (0.62–1.49) 1.20 (0.80–1.80) 1.18 (0.80–1.76) 1.00 (referent) 0.96 
Death from CVD (n18 26 26 33  
 Age- and energy-adjusted HR (95% CI) 0.44 (0.24–0.78) 0.67 (0.40–1.12) 0.65 (0.39–1.08) 1.00 (referent) 0.002 
 Multivariable-adjusted HR (95% CI)d 0.44 (0.24–0.82) 0.69 (0.40–1.20) 0.66 (0.38–1.12) 1.00 (referent) 0.005 

aPtrend was calculated using the continuous E-DII in the Cox proportional hazards regression model.

bStratified by age group at diagnosis (≤66 years old, >66 years old), ER status (positive, negative, others), race/ethnicity (white non-Hispanic, Hispanic/Latino, black/African American, other), and PR status (positive, negative, others) due to PH assumption violation and was adjusted for WHI component (OS, DM intervention, DM control), smoking status at baseline (never smoked, past smoker, current smoker, missing), income levels (<20,000, 20,000–49,999, ≥50,000, missing), cancer stage (localized, regional, distant, unknown), education (high school or below, some college, college, postgraduate, missing), years from cancer diagnosis to FFQ (continuous), baseline physical activity in MET-h/week (0, 0–3, 3–9, 9+), total energy intake per day (continuous), BMI at baseline (underweight, normal, overweight, obese, missing), hormone replacement use status at baseline (never used, current user, and past user), with the covariate of time-dependent status before and after postdiagnosis FFQ.

cStratified by education and PR status due to PH assumption violation and adjusted for other covariates listed in b.

dStratified by ER status due to PH assumption violation and adjusted for other covariates listed in b.

After stratifying by breast cancer hormone receptor status, a 27% lower risk of all-cause mortality was found among ER-positive (ER+) breast cancer cases in the lowest E-DII quartile compared with women in the highest quartile (HRQ1VSQ4 = 0.73; 95% CI, 0.54–0.97; Table 3). However, there was no association among ER-negative (ER) cases. Modest associations were observed for PR+ and PR cases comparing the most anti-inflammatory diets with the most proinflammatory diets (HRQ1VSQ4 = 0.84; 95% CI, 0.60–1.17 and HRQ1VSQ4 = 0.69; 95% CI, 0.42–1.14, respectively). When ER and PR status were combined, lower risk was observed among ER+ and/or PR+ cases, but not among ER and PR cases (Table 3).

Table 3.

Risk of all-cause mortality stratified by hormone receptor status of invasive breast cancer across quartiles of postdiagnosis E-DII in the WHI-DM and OS

Most anti-inflammatory dietMost proinflammatory diet
E-DII score quartile 1 (−6.81, −4.49)E-DII score quartile 2 (−4.48, −3.46)E-DII score quartile 3 (−3.45, −2.01)E-DII score quartile 4 (−2.00, 3.79)PtrendaPinteractionb
ER status 
 ER+; nc = 1,631 cases, n (%)d 100 (23.3) 113 (28.3) 97 (23.6) 113 (28.9)  0.44 
 Multivariable-adjusted HR (95% CI)e 0.73 (0.54–0.97) 0.95 (0.72–1.25) 0.77 (0.58–1.02) 1.00 (referent) 0.04  
 ER; nc = 299 cases, n (%)d 19 (30.2) 21 (28.4) 31 (43.7) 31 (34.1)   
 Multivariable-adjusted HR (95% CI)e 0.97 (0.49–1.91) 0.63 (0.31–1.25) 1.17 (0.64–2.11) 1.00 (referent) 0.53  
PR status 
 PR+; nc = 1,339 cases, n (%)d 84 (23.9) 85 (26.5) 81 (23.1) 88 (27.8)  0.57 
 Multivariable-adjusted HR (95% CI)e 0.84 (0.60–1.17) 0.96 (0.70–1.33) 0.78 (0.57–1.07) 1.00 (referent) 0.08  
 PR; nc = 546 cases, n (%)d 33 (26.4) 43 (30.1) 44 (35.2) 51 (33.3)   
 Multivariable-adjusted HR (95% CI)e 0.69 (0.42–1.14) 0.86 (0.54–1.39) 0.97 (0.61–1.53) 1.00 (referent) 0.50  
Combined ER and PR status 
 ER+ and/or PR+; nc = 1,663 cases, n(%)d 102 (23.4) 116 (28.5) 101 (24.2) 116 (28.8)   
 Multivariable-adjusted HR (95% CI)e 0.77 (0.57–1.02) 0.99 (0.75–1.30) 0.80 (0.60–1.05) 1.00 (referent) 0.05  
 ER and PR; nc = 259 cases, n (%)d 17 (32.1) 18 (27.7) 26 (40.6) 26 (33.8)   
 Multivariable-adjusted HR (95% CI)e 1.09 (0.53–2.24) 0.77 (0.38–1.54) 1.16 (0.62–2.18) 1.00 0.99  
Most anti-inflammatory dietMost proinflammatory diet
E-DII score quartile 1 (−6.81, −4.49)E-DII score quartile 2 (−4.48, −3.46)E-DII score quartile 3 (−3.45, −2.01)E-DII score quartile 4 (−2.00, 3.79)PtrendaPinteractionb
ER status 
 ER+; nc = 1,631 cases, n (%)d 100 (23.3) 113 (28.3) 97 (23.6) 113 (28.9)  0.44 
 Multivariable-adjusted HR (95% CI)e 0.73 (0.54–0.97) 0.95 (0.72–1.25) 0.77 (0.58–1.02) 1.00 (referent) 0.04  
 ER; nc = 299 cases, n (%)d 19 (30.2) 21 (28.4) 31 (43.7) 31 (34.1)   
 Multivariable-adjusted HR (95% CI)e 0.97 (0.49–1.91) 0.63 (0.31–1.25) 1.17 (0.64–2.11) 1.00 (referent) 0.53  
PR status 
 PR+; nc = 1,339 cases, n (%)d 84 (23.9) 85 (26.5) 81 (23.1) 88 (27.8)  0.57 
 Multivariable-adjusted HR (95% CI)e 0.84 (0.60–1.17) 0.96 (0.70–1.33) 0.78 (0.57–1.07) 1.00 (referent) 0.08  
 PR; nc = 546 cases, n (%)d 33 (26.4) 43 (30.1) 44 (35.2) 51 (33.3)   
 Multivariable-adjusted HR (95% CI)e 0.69 (0.42–1.14) 0.86 (0.54–1.39) 0.97 (0.61–1.53) 1.00 (referent) 0.50  
Combined ER and PR status 
 ER+ and/or PR+; nc = 1,663 cases, n(%)d 102 (23.4) 116 (28.5) 101 (24.2) 116 (28.8)   
 Multivariable-adjusted HR (95% CI)e 0.77 (0.57–1.02) 0.99 (0.75–1.30) 0.80 (0.60–1.05) 1.00 (referent) 0.05  
 ER and PR; nc = 259 cases, n (%)d 17 (32.1) 18 (27.7) 26 (40.6) 26 (33.8)   
 Multivariable-adjusted HR (95% CI)e 1.09 (0.53–2.24) 0.77 (0.38–1.54) 1.16 (0.62–2.18) 1.00 0.99  

aPtrend was calculated using the continuous E-DII in the Cox proportional hazards regression model.

bPinteraction was calculated by adding the cross-product of quartile E-DII and the effect modifier in the Cox proportional hazards regression model.

cThe total number of invasive breast cancers with the molecular subtype.

dNumber of deaths from any cause (proportion of deaths from any cause among the total number of invasive breast cancer cases within each quartile).

eModel was adjusted for age group at diagnosis (≤66 years old, >66 years old), race/ethnicity (white non-Hispanic, Hispanic/Latino, black/African American, other), WHI component (OS, DM intervention, DM control), smoking status at baseline (never smoked, past smoker, current smoker, missing), income levels (<20,000, 20,000–49,999, ≥50,000, missing), cancer stage (localized, regional, distant, unknown), education (high school or below, some college, college, postgraduate, missing), years from cancer diagnosis to FFQ (continuous), baseline physical activity in MET-h/week (0, 0–3, 3–9, 9+), total energy intake per day (continuous), BMI at baseline (underweight, normal, overweight, obese, missing), hormone replacement use status at baseline (never used, current user and past user), the alternative ER or PR status, except in the combined ER and PR analysis, with the covariate of time-dependent status before and after postdiagnosis FFQ.

Results from this large prospective study of 2,150 women who were postmenopause and diagnosed with invasive breast cancer suggest that consuming a more anti-inflammatory diet after cancer diagnosis is associated with lower CVD mortality risk, but not with all-cause mortality or breast cancer–specific mortality. In the stratified analyses by breast cancer hormone receptor status, an association between anti-inflammatory diet after cancer diagnosis and lower all-cause mortality was seen in the ER+ breast cancer cases and in the combined ER+ and/or PR+ tumors. To our knowledge, this is the first study to examine a post-cancer diagnosis dietary pattern with respect to inflammatory potential of the diet, as defined by E-DII, and all-cause and cause-specific mortality among women who were diagnosed with breast cancer and were postmenopausal.

Similar to our findings, most previous cohort studies have not found better dietary quality after breast cancer diagnosis to be associated with breast cancer–specific mortality but have observed lower risks of non–breast cancer mortality (8, 9, 11, 13–15). In a WHI study that examined postdiagnosis Healthy Eating Index (HEI)-2005 scores, having better postdiagnosis diet quality was associated with a 42% lower risk of death from non–breast cancer causes (HRQ4VSQ1 = 0.58; 95% CI, 0.38–0.87), but was not associated with breast cancer mortality (HRQ4VSQ1 = 0.91; 95% CI, 0.60–1.40) after a median follow-up of 9.6 years (11). In the Nurse's Health Study (NHS) with a median of 9.3 years follow-up, better adherence to the Dietary Approaches to Stop Hypertension (DASH) score and the Alternative-HEI-2010 after breast cancer diagnosis was associated with a 28% (RRQ5VSQ1 = 0.72; 95% CI, 0.53–0.99) and 43% (RRQ5VSQ1 = 0.57; 95% CI, 0.42–0.77) reduced risk of non–breast cancer mortality, respectively, among women who were, on average, 60 years old at diagnosis. Neither pattern was associated with breast cancer mortality (RRQ5VSQ1 = 0.85; 95% CI, 0.61–1.19 for DASH; and RRQ5VSQ1 = 1.07; 95% CI, 0.77–1.49 for AHEI-2010), and no significant effect modification by ER status was observed for either pattern (13). Results from another NHS study reported better adherence to four a priori dietary indices after breast cancer diagnosis, including AHEI, Diet Quality Index-Revised (DQIR), Recommended Food Score (RFS), and the alternate Mediterranean Diet Score (aMED), was not associated with breast cancer mortality (14). Similar results were also observed from two studies that examined a posteriori dietary patterns (prudent and Western dietary patterns) after breast cancer diagnosis among women with the majority being postmenopausal at diagnosis (8, 9). One study with median follow-up of 9 years found a 46% (RR Q5VSQ1 = 0.54; 95% CI, 0.31–0.95) lower risk of mortality from other causes than breast cancer in the highest compared with the lowest quintile of a prudent pattern (8), and the other study with a mean follow-up of 4.2 years found a 65% reduced non–breast cancer mortality risk associated with highest adherence to a prudent pattern (HR Q4VSQ1 = 0.35; 95% CI, 0.17–0.73; ref. 9). Anti-inflammatory DII scores were associated with better diet quality scores on other dietary indices, including AHEI, DASH, and HEI-2010 (55), and CVD is a leading cause of death for older breast cancer survivors (56). Thus, although other studies did not examine CVD mortality specifically, the reduced CVD mortality associated with better dietary quality observed in the current study is likely comparable with, and in agreement with, the lower non–breast cancer mortality risk observed in previous cohort studies (8, 9, 11, 13–15).

Biologically, atherosclerosis, the primary factor inducing CVD, is an inflammatory condition associated with elevated plasma levels of inflammatory cytokines (57, 58). Proinflammatory diets represented by higher DII scores have been related to both elevated CVD incidence (59, 60) and mortality risk (61–63). Data also suggest that higher levels of inflammation among patients with inflammatory diseases, such as polyarthritis, increase CVD mortality (58). However, one recent study using data from the Cancer Prevention Study-II Nutrition Cohort found contrasting findings, and reported after a mean follow-up time of 9.9 years; post-diagnostic diets consistent with the American Cancer Society recommendations for cancer prevention were not associated with CVD mortality among 4,452 breast cancer survivors whose mean age at diagnosis was 70 years (15). The difference compared with our findings in part may be explained by the different dietary patterns evaluated with perhaps diet-associated inflammation being a more important contributor to risk of CVD mortality than diets adhering to cancer prevention guidelines.

The null association we found for breast cancer–specific mortality was largely consistent with previous studies (8, 9, 11, 13, 14). It has been suggested that U-shaped rather than linear associations of several key dietary factors may better represent associations with breast cancer survival (64). However, in our previous study with 122,788 postmenopausal women in the WHI without prior cancer, a higher risk of death from breast cancer was associated with consumption of more proinflammatory diets at baseline (HRQ5VSQ1 = 1.33; 95% CI, 1.01–1.76; ref. 65). There is little evidence regarding the association between diet-associated inflammation after breast cancer diagnosis and breast cancer survival. Therefore, future cohort studies are warranted to examine whether postdiagnosis inflammatory potential of diet influences cancer survival among those who already have cancer.

Results from the Women's Healthy Eating and Living (WHEL) randomized controlled trial suggested that adoption of a healthy diet high in vegetables, fruit, and fiber and low in fat (also likely to be anti-inflammatory) did not reduce all-cause mortality among 3,088 women previously treated for early-stage breast cancer (HR = 0.91; 95% CI, 0.72–1.15; ref. 17). However, some observational studies have reported inverse associations for postdiagnosis diet quality and all-cause mortality, including the WHI (9–12). In the WHI-DM trial, women randomized to the diet characterized by low fat and increased intake of fruits, vegetables, and grains had lower risk of death after breast cancer compared with the usual diet comparison group (HR = 0.82; 95% CI, 0.70–0.96; ref. 18). The lack of a substantial association observed for all-cause mortality in the present study may reflect the sum effect of mixed associations of E-DII with risk of death from different disease outcomes.

Stratified analysis showed that a lower all-cause mortality risk was seen among ER+ breast cancer cases and among the combined ER+ and/or PR+ cases but not ER/PR cases. Our results were similar to another WHI study, which found an inverse relationship between postdiagnosis HEI-2005 score and all-cause mortality among ER+ women (HRQ4VSQ1 = 0.55; 95% CI, 0.38–0.79; Ptrend = 0.0009) but not among those with ER tumors (HR Q4VSQ1 = 1.14; 95% CI, 0.58–2.23; Ptrend = 0.81; ref. 11). It is suggested that women diagnosed with ER+ cancers generally have better prognosis than ER cancers (66), and thus, they are more likely to die of CVD, a leading cause of death among older breast cancer survivors (56). This may partially explain our observed stronger association among ER+ tumors given the significant association between E-DII and CVD mortality we identified in this study. As a result of small sample size in the ER+ tumors and ER+ and/or PR+ tumors, the stronger association seen among women with ER+ and/or PR+ tumors also may be due to chance. Future studies with sufficient sample size examining associations between postdiagnosis diet quality and mortality by breast cancer subtypes are warranted.

Strengths of our study include the use of the E-DII, which was specifically designed to assess inflammatory potential of diet while accounting for energy intake differences among individuals and can be applied to diverse populations. Other advantages include a large and well-characterized study population, the prospective nature of the study with a long follow-up to allow for accruing a large number of events, and inclusion of important covariates for adjustment. Detailed adjudication of cause of death minimized misclassification of outcomes. We conducted sensitivity analyses to rule out potential bias resulting from the effect of WHI dietary intervention and cancer treatment on postdiagnosis diet habits.

Limitations of our study included measurement error in using the WHI FFQ for dietary assessment (32), which is unavoidable with any dietary assessment tool (67). Diet and supplement use were assessed only at one time point after cancer diagnosis in our study, although they may change during the long study follow-up. The longitudinal stability of DII scores was investigated in the WHI-OS and DM control arm participants, and DII scores did not change significantly over time (68). Although we did not detect significant differences on important indicators related to proinflammatory diet or death, women who were excluded from our subset were older at diagnosis and had worse survival than included participants. Because we required a postdiagnosis FFQ, it is conceivable that women who had strongly proinflammatory diets developed more aggressive tumors and died before they could complete a postdiagnosis FFQ (65). In addition, 13 dietary components of the DII were not available from the WHI FFQ. All of these 13 missing components are anti-inflammatory, and some may have a beneficial effect on breast cancer survival (69). However, as previously reported, the range of DII scores may rely more on the amount of foods actually consumed rather than on the number of available DII components (65). Any misclassification in dietary inflammatory potential due to the missing dietary factors would likely be nondifferential and subsequently attenuate results toward the null. Inflammatory effect scores for each component of the E-DII were developed on the basis of a comprehensive review of the literature reporting each component's association with six inflammatory biomarkers. However, the absence of an established biomarker of energy-adjusted dietary inflammatory potential implies uncertainty concerning the properties of the corresponding E-DII assessment. In addition, there may be other inflammatory biomarkers beyond the six originally included in the development of the DII that are relevant to breast cancer survival, such as serum amyloid A (SAA; ref. 70). However, the literature relating dietary factors to SAA is limited at this time; so, inclusion of SAA in the construction of the DII is unlikely to change the scoring substantially. Although we adjusted for a large number of potential confounders, residual or unmeasured confounding may still be a possibility. In addition, as we did not have data on treatment, we used cancer stage and ER/PR status as proxy. Given the small sample size and small number of deaths in the sensitivity analyses and stratified analysis by ER and PR status, we cannot rule out that findings of these analyses were due to chance. Finally, our sample included only women who were postmenopausal. Future research on this topic should include large cohort studies with adequate treatment data and among younger women diagnosed with breast cancer.

In summary, in this large prospective study of women who were postmenopausal and diagnosed with invasive breast cancer, diet and supplement use with more anti-inflammatory potential after breast cancer diagnosis, as defined by lower E-DII scores, was associated with lower risk of death from CVD but not with breast cancer–specific or all-cause mortality. Our findings suggest that lowering the inflammatory potential of diet after cancer diagnosis may be important in reducing the risk of death from CVD among breast cancer survivors. Future large cohort studies are warranted to explore whether postdiagnosis inflammatory diet might affect outcomes in other cancers or affect survival in breast cancer survivors by specific subtypes.

N. Shivappa is the senior research scientist at Connecting Health Innovations. J.R. Hébert is the president and scientific director at and has ownership interest in Connecting Health Innovations LLC. No potential conflicts of interest were disclosed by the other authors.

Conception and design: F.K. Tabung, J.R. Hébert, S.E. Steck

Development of methodology: J. Zhang, J.R. Hébert, S.E. Steck

Acquisition of data (provided animals, acquired and managed patients, provided facilities, etc.): B. Caan

Analysis and interpretation of data (e.g., statistical analysis, biostatistics, computational analysis): J. Zheng, F.K. Tabung, J. Zhang, A.D. Liese, N. Shivappa, J.K. Ockene, C.H. Kroenke, J.R. Hébert, S.E. Steck

Writing, review, and/or revision of the manuscript: J. Zheng, F.K. Tabung, J. Zhang, A.D. Liese, J.K. Ockene, B. Caan, C.H. Kroenke, J.R. Hébert, S.E. Steck

Study supervision: J.K. Ockene, S.E. Steck

J. Zheng, F.K. Tabung, J. Zhang, J.K. Ockene, J.R. Hébert, and S.E. Steck were supported by grant number 318258 from the American Institute for Cancer Research. F.K. Tabung was supported by NCI grant K99CA207736. N. Shivappa and J.R. Hébert were supported by grant R44 DK103377 from the National Institute of Diabetes and Digestive and Kidney Diseases. The WHI program is funded by the National Heart, Lung, and Blood Institute, NIH, and U.S. Department of Health and Human Services through contracts HHSN268201600018C, HHSN268201600001C, HHSN268201600002C, HHSN268201600003C, and HHSN268201600004C.

We thank the Women's Health Initiative Investigators:

Program Office: (National Heart, Lung, and Blood Institute, Bethesda, MD) Jacques Rossouw, Shari Ludlam, Joan McGowan, Leslie Ford, and Nancy Geller

Clinical Coordinating Center: (Fred Hutchinson Cancer Research Center, Seattle, WA) Garnet Anderson, Ross Prentice, Andrea LaCroix, and Charles Kooperberg

Investigators and Academic Centers: (Brigham and Women's Hospital, Harvard Medical School, Boston, MA) JoAnn E. Manson; (MedStar Health Research Institute/Howard University, Washington, DC) Barbara V. Howard; (Stanford Prevention Research Center, Stanford, CA) Marcia L. Stefanick; (The Ohio State University, Columbus, OH) Rebecca Jackson; (University of Arizona, Tucson/Phoenix, AZ) Cynthia A. Thomson; (University at Buffalo, Buffalo, NY) Jean Wactawski-Wende; (University of Florida, Gainesville/Jacksonville, FL) Marian Limacher; (University of Iowa, Iowa City/Davenport, IA) Jennifer Robinson; (University of Pittsburgh, Pittsburgh, PA) Lewis Kuller; (Wake Forest University School of Medicine, Winston-Salem, NC) Sally Shumaker; (University of Nevada, Reno, NV) Robert Brunner; (University of Minnesota, Minneapolis, MN) Karen L. Margolis

Women's Health Initiative Memory Study: (Wake Forest University School of Medicine, Winston-Salem, NC) Mark Espeland

Additional Information: A full list of all the investigators who have contributed to Women's Health Initiative science appears at https://www.whi.org/researchers/Documents%20%20Write%20a%20Paper/WHI%20Investigator%20Long%20List.pdf.

We also thank the Women's Health Initiative staff and the trial participants for their outstanding dedication and commitment.

We acknowledge intellectual property rights regarding the DII and the intention of Connecting Health Innovations, LLC to license those rights from the University of South Carolina.

The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

1.
Siegel
RL
,
Miller
KD
,
Jemal
A
. 
Cancer Statistics, 2017
.
CA Cancer J Clin
2017
;
67
:
7
30
.
2.
American Cancer Society
.
Cancer Treatment & Survivorship Facts & Figures 2016–2017
.
Atlanta, GA
:
American Cancer Society
; 
2016
.
3.
Yancik
R
,
Wesley
MN
,
Ries
LA
,
Havlik
RJ
,
Edwards
BK
,
Yates
JW
. 
Effect of age and comorbidity in postmenopausal breast cancer patients aged 55 years and older
.
JAMA
2001
;
285
:
885
92
.
4.
Nagel
G
,
Wedding
U
,
Rohrig
B
,
Katenkamp
D
. 
The impact of comorbidity on the survival of postmenopausal women with breast cancer
.
J Cancer Res Clin Oncol
2004
;
130
:
664
70
.
5.
Bradshaw
PT
,
Stevens
J
,
Khankari
N
,
Teitelbaum
SL
,
Neugut
AI
,
Gammon
MD
. 
Cardiovascular disease mortality among breast cancer survivors
.
Epidemiology
2016
;
27
:
6
13
.
6.
Patterson
RE
,
Neuhouser
ML
,
Hedderson
MM
,
Schwartz
SM
,
Standish
LJ
,
Bowen
DJ
. 
Changes in diet, physical activity, and supplement use among adults diagnosed with cancer
.
J Am Diet Assoc
2003
;
103
:
323
8
.
7.
Thomson
CA
,
Flatt
SW
,
Rock
CL
,
Ritenbaugh
C
,
Newman
V
,
Pierce
JP
. 
Increased fruit, vegetable and fiber intake and lower fat intake reported among women previously treated for invasive breast cancer
.
J Am Diet Assoc
2002
;
102
:
801
8
.
8.
Kroenke
CH
,
Fung
TT
,
Hu
FB
,
Holmes
MD
. 
Dietary patterns and survival after breast cancer diagnosis
.
J Clin Oncol
2005
;
23
:
9295
303
.
9.
Kwan
ML
,
Weltzien
E
,
Kushi
LH
,
Castillo
A
,
Slattery
ML
,
Caan
BJ
. 
Dietary patterns and breast cancer recurrence and survival among women with early-stage breast cancer
.
J Clin Oncol
2009
;
27
:
919
26
.
10.
Vrieling
A
,
Buck
K
,
Seibold
P
,
Heinz
J
,
Obi
N
,
Flesch-Janys
D
, et al
Dietary patterns and survival in German postmenopausal breast cancer survivors
.
Br J Cancer
2013
;
108
:
188
92
.
11.
George
SM
,
Ballard-Barbash
R
,
Shikany
JM
,
Caan
BJ
,
Freudenheim
JL
,
Kroenke
CH
, et al
Better postdiagnosis diet quality is associated with reduced risk of death among postmenopausal women with invasive breast cancer in the women's health initiative
.
Cancer Epidemiol Biomarkers Prev
2014
;
23
:
575
83
.
12.
George
SM
,
Irwin
ML
,
Smith
AW
,
Neuhouser
ML
,
Reedy
J
,
McTiernan
A
, et al
Postdiagnosis diet quality, the combination of diet quality and recreational physical activity, and prognosis after early-stage breast cancer
.
Cancer Causes Control
2011
;
22
:
589
98
.
13.
Izano
MA
,
Fung
TT
,
Chiuve
SS
,
Hu
FB
,
Holmes
MD
. 
Are diet quality scores after breast cancer diagnosis associated with improved breast cancer survival?
Nutr Cancer
2013
;
65
:
820
6
.
14.
Kim
EH
,
Willett
WC
,
Fung
T
,
Rosner
B
,
Holmes
MD
. 
Diet quality indices and postmenopausal breast cancer survival
.
Nutr Cancer
2011
;
63
:
381
8
.
15.
McCullough
ML
,
Gapstur
SM
,
Shah
R
,
Campbell
PT
,
Wang
Y
,
Doyle
C
, et al
Pre- and postdiagnostic diet in relation to mortality among breast cancer survivors in the CPS-II Nutrition Cohort
.
Cancer Causes Control
2016
;
27
:
1303
14
.
16.
Chlebowski
RT
,
Blackburn
GL
,
Thomson
CA
,
Nixon
DW
,
Shapiro
A
,
Hoy
MK
, et al
Dietary fat reduction and breast cancer outcome: interim efficacy results from the Women's Intervention Nutrition Study
.
J Natl Cancer Inst
2006
;
98
:
1767
76
.
17.
Pierce
JP
,
Natarajan
L
,
Caan
BJ
,
Parker
BA
,
Greenberg
ER
,
Flatt
SW
, 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
2007
;
298
:
289
98
.
18.
Chlebowski
RT
,
Aragaki
AK
,
Anderson
GL
,
Thomson
CA
,
Manson
JE
,
Simon
MS
, et al
Low-fat dietary pattern and breast cancer mortality in the Women's Health Initiative Randomized Controlled Trial
.
J Clin Oncol
2017
;
35
:
2919
26
.
19.
Keibel
A
,
Singh
V
,
Sharma
MC
. 
Inflammation, microenvironment, and the immune system in cancer progression
.
Curr Pharm Des
2009
;
15
:
1949
55
.
20.
Cooney
RV
,
Chai
W
,
Franke
AA
,
Wilkens
LR
,
Kolonel
LN
,
Le Marchand
L
. 
C-reactive protein, lipid-soluble micronutrients, and survival in colorectal cancer patients
.
Cancer Epidemiol Biomarkers Prev
2013
;
22
:
1278
88
.
21.
Pearson
TA
,
Mensah
GA
,
Alexander
RW
,
Anderson
JL
,
Cannon
RO
 III
,
Criqui
M
, et al
Markers of inflammation and cardiovascular disease: application to clinical and public health practice: A statement for healthcare professionals from the Centers for Disease Control and Prevention and the American Heart Association
.
Circulation
2003
;
107
:
499
511
.
22.
Erlinger
TP
,
Muntner
P
,
Helzlsouer
KJ
. 
WBC count and the risk of cancer mortality in a national sample of U.S. adults: results from the Second National Health and Nutrition Examination Survey mortality study
.
Cancer Epidemiol Biomarkers Prev
2004
;
13
:
1052
6
.
23.
Coussens
LM
,
Werb
Z
. 
Inflammation and cancer
.
Nature
2002
;
420
:
860
7
.
24.
Shivappa
N
,
Steck
SE
,
Hurley
TG
,
Hussey
JR
,
Hebert
JR
. 
Designing and developing a literature-derived, population-based dietary inflammatory index
.
Public Health Nutr
2014
;
17
:
1689
96
.
25.
Anderson
G
,
Cummings
S
,
Freedman
LS
,
Furberg
C
,
Henderson
M
,
Johnson
SR
, et al
Design of the Women's Health Initiative clinical trial and observational study
.
Control Clin Trials
1998
;
19
:
61
109
.
26.
Hays
J
,
Hunt
JR
,
Hubbell
FA
,
Anderson
GL
,
Limacher
M
,
Allen
C
, et al
The Women's Health Initiative recruitment methods and results
.
Ann Epidemiol
2003
;
13
:
S18
77
.
27.
Langer
RD
,
White
E
,
Lewis
CE
,
Kotchen
JM
,
Hendrix
SL
,
Trevisan
M
. 
The Women's Health Initiative Observational Study: baseline characteristics of participants and reliability of baseline measures
.
Ann Epidemiol
2003
;
13
:
S107
21
.
28.
Kristal
AR
,
Feng
Z
,
Coates
RJ
,
Oberman
A
,
George
V
. 
Associations of race/ethnicity, education, and dietary intervention with the validity and reliability of a food frequency questionnaire The Women's Health Trial Feasibility Study in minority populations
.
Am J Epidemiol
1997
;
146
:
856
69
.
29.
Henderson
MM
,
Kushi
LH
,
Thompson
DJ
,
Gorbach
SL
,
Clifford
CK
,
Insull
W
, et al
Feasibility of a randomized trial of a low-fat diet for the prevention of breast cancer: dietary compliance in the Women's Health Trial Vanguard Study
.
Prev Med
1990
;
19
:
115
33
.
30.
White
E
,
Shattuck
AL
,
Kristal
AR
,
Urban
N
,
Prentice
RL
,
Henderson
MM
, et al
Maintenance of a low-fat diet: follow-up of the Women's Health Trial
.
Cancer Epidemiol Biomarkers Prev
1992
;
1
:
315
23
.
31.
Kristal
AR
,
Patterson
RE
,
Glanz
K
,
Heimendinger
J
,
Hebert
JR
,
Feng
Z
, et al
Psychosocial correlates of healthful diets: baseline results from the Working Well Study
.
Prev Med
1995
;
24
:
221
8
.
32.
Patterson
RE
,
Kristal
AR
,
Tinker
LF
,
Carter
RA
,
Bolton
MP
,
Agurs-Collins
T
. 
Measurement characteristics of the Women's Health Initiative food frequency questionnaire
.
Ann Epidemiol
1999
;
9
:
178
87
.
33.
Schakel
S
,
Sievert
Y
,
Buzzard
I
. 
Sources of data for developing and maintaining a nutrient database
.
J Am Diet Assoc
1988
;
88
:
1268
71
.
34.
Patterson
RE
,
Kristal
A
,
Rodabough
R
,
Caan
B
,
Lillington
L
,
Mossavar-Rahmani
Y
, et al
Changes in food sources of dietary fat in response to an intensive low-fat dietary intervention: early results from the Women's Health Initiative
.
J Am Diet Assoc
2003
;
103
:
454
60
.
35.
Shivappa
N
,
Steck
SE
,
Hurley
TG
,
Hussey
JR
,
Ma
Y
,
Ockene
IS
, et al
A population-based dietary inflammatory index predicts levels of C-reactive protein in the Seasonal Variation of Blood Cholesterol Study (SEASONS)
.
Public Health Nutr
2014
;
17
:
1825
33
.
36.
Tabung
FK
,
Steck
SE
,
Zhang
J
,
Ma
Y
,
Liese
AD
,
Agalliu
I
, et al
Construct validation of the dietary inflammatory index among postmenopausal women
.
Ann Epidemiol
2015
;
25
:
398
405
.
37.
Shivappa
N
,
Hébert
JR
,
Rietzschel
ER
,
De Buyzere
ML
,
Langlois
M
,
Debruyne
E
, et al
Associations between dietary inflammatory index and inflammatory markers in the Asklepios Study
.
Br J Nutr
2015
;
113
:
665
71
.
38.
Cavicchia
PP
,
Steck
SE
,
Hurley
TG
,
Hussey
JR
,
Ma
Y
,
Ockene
IS
, et al
A new dietary inflammatory index predicts interval changes in serum high-sensitivity C-reactive protein
.
J Nutr
2009
;
139
:
2365
72
.
39.
Wirth
MD
,
Shivappa
N
,
Davis
L
,
Hurley
T
,
Ortaglia
A
,
Drayton
R
, et al
Construct validation of the dietary inflammatory index among African Americans
.
J Nutr Health Aging
2017
;
21
:
487
91
.
40.
Wassertheil-Smoller
S
,
McGinn
AP
,
Budrys
N
,
Chlebowski
R
,
Ho
GY
,
Johnson
KC
, et al
Multivitamin and mineral use and breast cancer mortality in older women with invasive breast cancer in the women's health initiative
.
Breast Cancer Res Treat
2013
;
141
:
495
505
.
41.
Wirth
MD
,
Shivappa
N
,
Hurley
TG
,
Hébert
JR
. 
Association between previously diagnosed circulatory conditions and a dietary inflammatory index
.
Nutr Res
2016
;
36
:
227
33
.
42.
Irwin
ML
,
McTiernan
A
,
Manson
JE
,
Thomson
CA
,
Sternfeld
B
,
Stefanick
ML
, et al
Physical activity and survival in postmenopausal women with breast cancer: results from the women's health initiative
.
Cancer Prev Res
2011
;
4
:
522
9
.
43.
Seidell
JC
,
Flegal
KM
. 
Assessing obesity: classification and epidemiology
.
Br Med Bull
1997
;
53
:
238
52
.
44.
Curb
JD
,
McTiernan
A
,
Heckbert
SR
,
Kooperberg
C
,
Stanford
J
,
Nevitt
M
, et al
Outcomes ascertainment and adjudication methods in the Women's Health Initiative
.
Ann Epidemiol
2003
;
13
:
S122
8
.
45.
Cunningham
J
,
Hankey
B
,
Lyles
B
,
Percy
C
,
Ries
L
,
Seiffert
J
.
The SEER program code manual
.
Bethesda, MD
:
NCI
; 
1992
.
46.
Pitman
EJG
. 
Significance tests which may be applied to samples from any populations: III. The analysis of variance test
.
Biometrika
1938
;
29
:
322
35
.
47.
Schoenfeld
D
. 
Chi-squared goodness-of-fit tests for the proportional hazards regression model
.
Biometrika
1980
;
67
:
145
53
.
48.
Hess
KR
. 
Assessing time-by-covariate interactions in proportional hazards regression models using cubic spline functions
.
Stat Med
1994
;
13
:
1045
62
.
49.
Therneau
TM
,
Grambsch
PM
.
Modeling survival data: extending the Cox model
.
New York, NY
:
Springer Science & Business Media
; 
2000
.
50.
Fisher
B
,
Jeong
JH
,
Bryant
J
,
Anderson
S
,
Dignam
J
,
Fisher
ER
, et al
Treatment of lymph-node-negative, oestrogen-receptor-positive breast cancer: long-term findings from National Surgical Adjuvant Breast and Bowel Project randomised clinical trials
.
Lancet
2004
;
364
:
858
68
.
51.
Goldhirsch
A
,
Wood
WC
,
Gelber
RD
,
Coates
AS
,
Thurlimann
B
,
Senn
HJ
. 
Meeting highlights: updated international expert consensus on the primary therapy of early breast cancer
.
J Clin Oncol
2003
;
21
:
3357
65
.
52.
Smith
AW
,
Alfano
CM
,
Reeve
BB
,
Irwin
ML
,
Bernstein
L
,
Baumgartner
K
, et al
Race/ethnicity, physical activity, and quality of life in breast cancer survivors
.
Cancer Epidemiol Biomarkers Prev
2009
;
18
:
656
63
.
53.
Fung
TT
,
Hu
FB
,
McCullough
ML
,
Newby
P
,
Willett
WC
,
Holmes
MD
. 
Diet quality is associated with the risk of estrogen receptor–negative breast cancer in postmenopausal women
.
J Nutr
2006
;
136
:
466
72
.
54.
Kushi
LH
,
Potter
JD
,
Bostick
RM
,
Drinkard
CR
,
Sellers
TA
,
Gapstur
SM
, et al
Dietary fat and risk of breast cancer according to hormone receptor status
.
Cancer Epidemiol Biomarkers Prev
1995
;
4
:
11
9
.
55.
Wirth
MD
,
Hébert
JR
,
Shivappa
N
,
Hand
GA
,
Hurley
TG
,
Drenowatz
C
, et al
Anti-inflammatory Dietary Inflammatory Index scores are associated with healthier scores on other dietary indices
.
Nutr Res
2016
;
36
:
214
9
.
56.
Patnaik
JL
,
Byers
T
,
DiGuiseppi
C
,
Dabelea
D
,
Denberg
TD
. 
Cardiovascular disease competes with breast cancer as the leading cause of death for older females diagnosed with breast cancer: a retrospective cohort study
.
Breast Cancer Res
2011
;
13
:
1
.
57.
Frostegård
J
. 
Immunity, atherosclerosis and cardiovascular disease
.
BMC Med
2013
;
11
:
117
.
58.
Goodson
NJ
,
Wiles
NJ
,
Lunt
M
,
Barrett
EM
,
Silman
AJ
,
Symmons
DP
. 
Mortality in early inflammatory polyarthritis: cardiovascular mortality is increased in seropositive patients
.
Arthritis Rheum
2002
;
46
:
2010
9
.
59.
Ramallal
R
,
Toledo
E
,
Martínez-González
MA
,
Hernández-Hernández
A
,
García-Arellano
A
,
Shivappa
N
, et al
Dietary inflammatory index and incidence of cardiovascular disease in the SUN cohort
.
PLoS One
2015
;
10
:
e0135221
.
60.
Garcia-Arellano
A
,
Ramallal
R
,
Ruiz-Canela
M
,
Salas-Salvadó
J
,
Corella
D
,
Shivappa
N
, et al
Dietary inflammatory index and incidence of cardiovascular disease in the PREDIMED Study
.
Nutrients
2015
;
7
:
4124
38
.
61.
Shivappa
N
,
Steck
SE
,
Hussey
JR
,
Ma
Y
,
Hebert
JR
. 
Inflammatory potential of diet and all-cause, cardiovascular, and cancer mortality in National Health and Nutrition Examination Survey III Study
.
Eur J Nutr
2017
;
56
:
683
92
.
62.
Shivappa
N
,
Blair
CK
,
Prizment
AE
,
Jacobs
DR
 Jr
,
Steck
SE
,
Hébert
JR
. 
Association between inflammatory potential of diet and mortality in the Iowa Women's Health study
.
Eur J Nutr
2016
;
55
:
1491
502
.
63.
Deng
FE
,
Shivappa
N
,
Tang
Y
,
Mann
JR
,
Hebert
JR
. 
Association between diet-related inflammation, all-cause, all-cancer, and cardiovascular disease mortality, with special focus on prediabetics: findings from NHANES III
.
Eur J Nutr
2017
;
56
:
1085
93
.
64.
Goodwin
PJ
,
Ennis
M
,
Pritchard
KI
,
Koo
J
,
Trudeau
ME
,
Hood
N
. 
Diet and breast cancer: evidence that extremes in diet are associated with poor survival
.
J Clin Oncol
2003
;
21
:
2500
7
.
65.
Tabung
FK
,
Steck
SE
,
Liese
AD
,
Zhang
J
,
Ma
Y
,
Caan
B
, et al
Association between dietary inflammatory potential and breast cancer incidence and death: results from the Women's Health Initiative
.
Br J Cancer
2016
;
114
:
1277
85
.
66.
Dunnwald
LK
,
Rossing
MA
,
Li
CI
. 
Hormone receptor status, tumor characteristics, and prognosis: a prospective cohort of breast cancer patients
.
Breast Cancer Res
2007
;
9
:
1
.
67.
Willett, Walter
.
Nutritional Epidemiology: second edition
.
New York
:
Oxford University Press
, 
1998
.
68.
Tabung
F
,
Steck
S
,
Zhang
J
,
Ma
Y
,
Liese
A
,
Tylavsky
F
, et al
Longitudinal changes in the dietary inflammatory index: an assessment of the inflammatory potential of diet over time in postmenopausal women
.
Eur J Clin Nutr
2016
;
70
:
1374
80
.
69.
Fink
BN
,
Steck
SE
,
Wolff
MS
,
Britton
JA
,
Kabat
GC
,
Gaudet
MM
, et al
Dietary flavonoid intake and breast cancer survival among women on Long Island
.
Cancer Epidemiol Biomarkers Prev
2007
;
16
:
2285
92
.
70.
Pierce
BL
,
Ballard-Barbash
R
,
Bernstein
L
,
Baumgartner
RN
,
Neuhouser
ML
,
Wener
MH
, et al
Elevated biomarkers of inflammation are associated with reduced survival among breast cancer patients
.
J Clin Oncol
2009
;
27
:
3437
44
.