Review Article | Published:

Meta-analysis of the association between the dietary inflammatory index (DII) and breast cancer risk

European Journal of Clinical Nutrition (2018) | Download Citation



Recent studies have reported mixed results on the association between the pro-inflammatory dietary index and risk of breast cancer. We perform this comprehensive meta-analysis to figure out whether high dietary inflammatory index (DII) score is a risk factor for the occurrence of breast cancer.


We comprehensively searched the PubMed, EMBASE and Cochrane databases to identify included studies updated to September 12, 2017. All studies that reported risk estimates by comparing the highest DII score to the lowest were assessed.


A total of seven observational studies were identified: three case controls and four cohorts, involving 319,993 participants. Overall, the meta-analysis reported that individuals with the highest DII score were associated with a 25% increased risk of breast cancer versus those with the lowest DII score (relative risk [RR] = 1.25; 95% confidence interval [CI] 1.09–1.44; I2 = 82.7%, p = 0.000). Upon stratified analysis, significant positive associations remained for postmenopausal women (RR = 1.15; 95% CI 1.02–1.30; p = 0.020), case-control studies (RR = 1.68; 95% CI 1.13–2.49; p = 0.010), Asia (RR = 2.30; 95% CI 1.7–3.12; p = 0.0031) and Europe (RR = 1.26; 95% CI 1.01–1.58; p = 0.0477). When analysed on hormonal receptor status, 36% increased risk was explored for hormone-receptor negative.


This meta-analysis suggested that more pro-inflammatory diets (higher DII scores) are associated with increased breast cancer incidence. However, the research is not about significant associations but about moderate effect sizes.


Breast cancer is the leading cause of cancer deaths in women worldwide, representing 25% of all cancer cases [1]. Chronic inflammation is important in the carcinogenesis process [2]. As a persistent condition, in which tissue destruction and repair occur simultaneously [3], chronic inflammation involves continuous recruitment of pro-inflammatory cytokines and elevated levels of these cytokines, such as C-reactive protein (CRP), tumour necrosis factor-α (TNF-α) and interleukin-6 (IL6), which has been suggested as an important player in breast cancer initiation, promotion and metastasis [4,5,6,7].

Additionally, with the increasing support of scientific evidence, specific dietary components show a correlation with inflammation [8]. There is considerable interest in studying the inflammatory potential of specific food and dietary patterns and assessing the extent to which higher dietary inflammation is associated with cancer risk. The Mediterranean Diet (MD), consisting of large amounts of monounsaturated fatty acids, fruits, vegetables, and whole grains, has been extensively assessed and suggested as a pivotal factor in preventing tumourigenesis through inflammatory pathways [9, 10]. In contrast, ‘high-alcohol’ dietary pattern [11], higher dietary glycaemic index [12] and more saturated fat intake [13] were considered to increase inflammation and are associated with increased breast cancer risk. However, a more accurate assessment of the inflammatory potential of food and dietary patterns and the assessment of the extent to which higher dietary inflammation is associated with cancer risk are of great importance.

Since 2014, a novel dietary inflammatory index (DII) has been developed to quantify the inflammatory potential of individual diets. It is a literature-derived dietary index that evaluates whether dietary quality has a positive or negative effect on inflammation potential, by predicting the levels of inflammatory markers, such as CRP, IL-6 and homocysteine [14]. A higher DII score will be obtained with a pro-inflammatory diet, and vice versa, and more anti-inflammatory diets, similar to the MD, show lower DII scores [15]. Although DII is widely used in the risk of several diseases, and partial correlation has been revealed, such as for colorectal cancer (CRC) and cardiovascular disease [16,17,18], to our knowledge, there is currently no systematic review or meta-analysis that has focused exclusively on the relationship between DII scores and breast cancer risk. Any effect of DII on breast cancer risk would therefore have important public health and clinical implications.

We conducted this research to summarise all available evidence [19,20,21,22,23,24,25] from case-control and cohort studies regarding the association between DII score and breast cancer incidence and to provide a quantitative assessment. If the results show that DII is a key risk predictor for breast cancer, reducing diet-related chronic inflammation will be a critical part of comprehensive cancer prevention strategies.


Search strategy

To identify relevant studies on DII and breast cancer, electronic databases PubMed, EMBASE and Cochrane were searched and updated to September 12, 2017. Relevant terms related to DII (dietary inflammatory index, inflammatory diet, anti-inflammatory diet, inflammatory potential of diet, and dietary score) and breast cancer (breast cancer, breast cancers, breast neoplasm, breast neoplasms, breast carcinoma, breast carcinomas, breast tumour, breast tumours, breast sarcoma, breast sarcomas, breast carcinogenesis, mammary cancer, mammary cancers, mammary neoplasm, mammary neoplasms, mammary carcinoma, mammary carcinomas, mammary tumour, and mammary tumours) were combined in various forms to further adapt for each database (complete search strategies are shown in Appendix Table S1). The meta-analysis was restricted to only articles published in English. To test the sensitivity of the search strategy and find any other relevant publications, reference lists of multiple articles and reviews were checked manually.

Study selection

The inclusion criteria were 1) humans from the representative population, 2) inflammatory potential of diet measured by the DII score, 3) the association between breast cancer risk and dietary inflammatory index, 4) risk estimates reported for the highest DII score compared with the lowest, 5) result adjusted for multiple covariates and 6) case-control or prospective observational study. Any study was excluded if meeting any of the following criteria: 1) DII score measured as a continuous variable, or 2) unavailable full text, such as conference abstract, comment or review article.

Data extraction and quality assessment

For each study, the following population characteristics and study data were extracted: the first author’s surname, year of publication, country, study design, sample size, gender, age (range/mean), DII score evaluation and comparison, menopausal and hormone receptor status, risk estimate with most fully adjusted, adjustment for covariate, and followup. The methodological quality of eligible studies was assessed using the Newcastle-Ottawa Scale (NOS) [26], in which quality was judged according to selection (four stars), comparability (two stars) and outcome (three stars). Studies of 7 stars or more indicated high quality and those with scores lower than 3 stars were defined as having low quality (detailed scoring strategies are shown in Appendix Table S2). Two authors independently performed data extraction and quality assessment, and a third investigator was chosen if any disagreement existed after discussion.

Statistical analysis

We calculated pooled RR together with the 95% confidence interval (95% CI) for the highest versus the lowest categories of DII score, under the premise that the original odds ratios (ORs) and hazard rates (HRs) reported in the text were seen as equivalent estimations for relative risks (RRs) [27]. The stratified analyses of hormonal receptor status and menopausal status in eligibility articles were deemed a separate set of studies, and data were extracted independently. The statistical heterogeneity across studies was assessed using the Cochran Q and I2 statistics with significance defined as Q test ≤ 0.10 or I2 > 50% [28]. The random-effects model was selected as a result of the existence of significant heterogeneity; if not, the fixed-effect model was performed to combine results. Meta-regression and subgroup analyses based on menopausal status, race, research method and adjustment factors were carried out to explore the potential sources of heterogeneity across studies. One study was excluded in each turn by a sensitivity analysis to investigate the robustness of summary results. Funnel plots, Begg’s test and Egger’s test were conducted to assess publication bias based on visual observation and P value. All of the above operations were implemented through the Stata 12.0 software (Stata Corporation, College Station, Texas, USA).


Literature search and study characteristics

The detailed retrieval and screening process of search is shown in Fig. 1. Finally, seven articles were chosen for the meta-analysis after reading the full text. The seven articles included studies [19,20,21,22,23,24,25] that were published between 2015 and 2017, covering 319,993 individuals in total. Among all seven studies, three studies [21, 22, 25] enroled both premenopausal and postmenopausal women, and four studies [19, 20, 23, 24] researched only postmenopausal women. Three studies [19, 21, 22] had a case-control design, and the remaining four were cohort studies [20, 23,24,25]. Three studies were performed in North America [20, 23, 24], three in Europe [19, 22, 25], and one in Asia [21]. Four [19,20,21, 24] of the seven included studies were conducted as a stratified study of hormonal receptor status, and at least one positive with oestrogen-receptor (ER) or progesterone receptor (PR) was defined as hormone-receptor positive. More detailed information on the seven included studies is provided in Table 1. The DII scores were all evaluated by authoritative food frequency questionnaires (FFQs). All seven studies included achieved assessment with 6–8 stars and a mean score of NOS, which was 6.86, suggesting moderate methodological quality.

Fig. 1
Fig. 1

Flow chart of article screening and selection process

Table 1 Characteristics of included studies in the meta-analysis

Overall meta-analysis of DII score and breast cancer risk

The pooled RR of overall studies suggested that compared to individuals in the lowest DII score category, those in the highest category had a 25% increased risk of breast cancer (RR = 1.25; 95% CI 1.09–1.44; p = 0.001) in a random effect model (Fig. 2). Meanwhile, significant heterogeneity between studies was revealed (I2 = 82.7%, p = 0.000). Meta-regression analyses showed that design method (p < 0.05) and region (p < 0.05) may partly explain the potential heterogeneity between studies.

Fig. 2
Fig. 2

Forest plot for relative risk (RR) of the highest compared with the lowest category of DII and breast cancer. Note: a represents different hormone receptor states; b represents different design methods

Subgroup analyses

Subgroup analyses on menopausal status, region, design method and adjustment factors were conducted because of the high heterogeneity of study results (Table 2). For postmenopausal women, the summary RR indicated a significant positive association between high DII category and breast cancer risk (RR = 1.15; 95% CI 1.02–1.30; p = 0.020) but not in premenopausal women (RR = 1.58; 95% CI 0.88–2.83; p = 0.123). Stratified study of design method showed that cohort studies had lower heterogeneity (I2 = 36.1%; p = 0.181) but with no significantly increased risk (RR = 1.06; 95% CI: 0.98–1.14; p = 0.134). Additionally, positive association in case-control studies (RR = 1.68; 95% CI 1.13–2.49; p = 0.010) was revealed while with higher heterogeneity (I2 = 87.8%; p = 0.000). Independent analysis for individuals from Asia (RR = 2.30; 95% CI 1.7–3.12; p = 0.0031) and Europe (RR = 1.26; 95% CI 1.01–1.58; p = 0.0477) showed changeless increased risks but not for North America (RR = 1.04; 95% CI 0.96–1.12; p = 0.305). Positive association was still observed in the stratified study of adjustment for family history of breast cancer (RR = 1.38; 95% CI: 1.11–1.70; p = 0.003) but not in studies that adjusted for oophorectomy, nonsteroidal anti-inflammatory drugs (NSAIDs) and smoking. All of the above calculations were based on a random-effects model.

Table 2 Subgroups and additional analyses of studies reporting the risk of breast cancer for the highest versus the lowest category of DII (analyses based on 7 studies consisting of 10 databases)

Publication bias and sensitivity analyses

Some evidence for publication bias was suggested based on the funnel plot (Fig. 3), Egger’s test (p = 0.011) and Begg’s test (p = 0.032). Sensitivity analysis (Fig. 4), excluding the study of Qing Huang et al., obtained slightly affected results (RR = 1.16; 95% CI 1.03–1.3; p = 0.010; I2 = 73.5%, p = 0.000). Meanwhile, omission of another individual study also made no significant impact, which revealed the stability of the present research.

Fig. 3
Fig. 3

Funnel plot of studies on DII and breast cancer

Fig. 4
Fig. 4

Sensitivity analysis was performed by removing each study in turn and recalculating the pooled relative risk (RR) estimates

Hormonal receptor status-specific associations

Four studies [19,20,21, 24] explored the stratified results of hormonal receptor status (detailed information are shown in Appendix Table S3). The combined RR was 1.36 (95% CI 1.01–1.81; I2 = 62.5%; p = 0.021) for hormone-receptor negative and 1.14 (95% CI 0.93–1.39; I2 = 81.8%; p = 0.000) for hormone-receptor positive (Fig. 5).

Fig. 5
Fig. 5

Forest plot for relative risk (RR) of the highest compared with the lowest category of DII and breast cancer with different hormonal receptor statuses. Note: c, d, e, and f represent stratification results of different hormone receptor states in the same study


This meta-analysis summarises the currently published literature examining the association between DII and breast cancer risk. The main finding suggests that more pro-inflammatory diets, as estimated by the higher DII score, are independently associated with an increased risk of breast cancer. Participants with the highest category of DII (maximal pro-inflammatory) had a breast cancer risk of up to 25%. The data related to hormonal receptor differences were extracted individually in our study; a higher DII score dietary pattern was associated with an increase in the risk of breast cancer when the results of all studies were pooled, and this positive incidence remained when the results of ER- PR- alone were pooled. This result may be due to the fact that the potential influence of dietary factors may be difficult to detect in hormone receptor-positive tumours, given the strong influence of hormonal factors. In contrast, these factors may have a relatively large impact on hormone receptor-negative tumours [29, 30]. These subtype results may be particularly important because identification of preventive factors for hormone receptor-negative breast cancer may help reduce the burden of breast cancer since these tumours have higher malignancy than hormone receptor-positive tumours.

In the subgroup analysis, the following results were obtained by stratifying by study design, region and menopausal status. Compared to cohort studies, the case-control studies identified a more significant association between DII score and risk of breast cancer. However, case-control studies are more prone to biases, especially in selecting cases and comparable controls, as well as in the possible differential accuracy of recalling previous information. Moreover, a positive association between DII and the incidence of breast cancer only appeared in postmenopausal studies, which is consistent with previous studies [31, 32]. Also pivotal is the finding that the significant association was only observed among studies conducted in Europe and Asia but not in North America. Finally, we further explored the adjustment of several main risk factors using subgroup analyses, including family history of breast cancer, NSAIDs use, oophorectomy status and smoking status [33,34,35,36]. The results suggested that the positive relationship was only in the subgroup adjusted for family history; the remaining three subgroups showed no significant association.

Unlike traditional dietary scores, the DII is a new literature-derived population-based dietary index that specifically focuses on dietary inflammatory potential [14, 37]. This index does not depend on an individual study or a few studies within the same or similar populations but on the basis of nearly 2,000 articles focusing on laboratory and human studies around the world in order to assess a total of 45 food parameters, including various macronutrients, vitamins, minerals, flavonoids, and specific food items [38]. In practice, DII score is usually computed from dietary intake, which is assessed using a validated food frequency questionnaire (FFQ) or from historical dietary records. In addition to the consistent association of DII with CRC and CVD [16,17,18], inflammation markers have successfully been validated in several studies among different populations [39,40,41]. All of these results provide evidence that DII is powerful in terms of its ability to relate to chronic inflammation. As the first step in the prevention and treatment of breast cancer, following a healthy dietary pattern is important.

Several limitations should be noted in this meta-analysis. First, all DII scores were derived from self-reported FFQs or historical dietary records. As part of individual surveys, convenience, low cost, and other characteristics may be partially offset by limitations, such as the inherent recall bias and limitations in assessing culture-specific food items [42]. Second, the DII score was estimated at baseline; these data reflect short-term conditions rather than long-term habits and might change during the followup duration. However, it is worth mentioning that the adult diet varies relatively little [43, 44]. Third, substantial heterogeneity was observed across studies pooling the onset risk. Therefore, we used random-effects models to account for heterogeneity; further subgroup analyses and meta-regression analyses were performed to avoid confounding effects and to explore the sources of heterogeneity. The observed heterogeneity may be related to the types of study design, geographic area, whether to adjust for smoking, the ovariectomy status and the use of NSAIDs. Nevertheless, the potential confounders, small samples and limited studies that cannot be ruled out are unavoidable as a limitation affecting our results. For example, in the adjustment of the latter two risk factors (NSAID use and oophorectomy status), the adjusted subgroups only contain the same study (Tabung 2016), which may lead to potential publication bias. Moreover, this meta-analysis is further limited by the absence of studies from Australia, Africa, and South America, which may result in the failure to generalise the results on a global scale. And due to insufficient significant correlation, results should be treated with caution. Despite the limitations, several strengths should also be mentioned. Since the DII used in each study was calculated in the same manner, comparability is increased. Furthermore, sensitivity analyses showed no significant change in the overall risk estimates after each study was sequentially deleted.


To our knowledge, our meta-analysis is the most comprehensive within the context of a systematic review, including all available evidence from observational studies and focusing exclusively on the relationship between breast cancer risk and DII. Positive associations were observed between higher DII and breast cancer risk.


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This research was supported by National Natural Science Foundation of China (No. 81473513).

Author contributions

LW and CL contributed equally to this paper and are co-first authors. LW, CL and CS designed the research; FF, LL and TZ searched databases and collected full-text papers; LW, CL and CZ interpreted and extracted the data; LW, CL and LZ wrote the manuscript; CS and JT secured funds, supervised the study and made critical revision of the manuscript. All authors have read and approved the final manuscript.

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Author notes

  1. These author contributed equally: Lu Wang, Cun Liu.


  1. College of Traditional Chinese Medicine, Shandong University of Traditional Chinese Medicine, 250014, Jinan, Shandong Province, China

    • Lu Wang
    • , Cun Liu
    •  & Tingting Zhang
  2. Department of Oncology, Weifang Traditional Chinese Hospital, 261041, Weifang, Shandong Province, China

    • Chao Zhou
    • , Jing Zhuang
    • , Shifeng Tang
    • , Fubin Feng
    •  & Lijuan Liu
  3. University of California, San Francisco, CA, 94158, USA

    • Jintai Yu
  4. Evidence-Based Medicine Center, School of Basic Medical Sciences, Lanzhou University, 730000, Lanzhou, Gansu Province, China

    • Jinhui Tian
  5. Department of Oncology, Affilited Hospital of Weifang Medical University, 261031, Weifang, Shandong Province, China

    • Changgang Sun


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Correspondence to Changgang Sun.

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