Abstract
Background:
Inflamation is widely known as an adaptive pathophysiological response in a variety of cancers. There is an expanding body of research on the key role of diet in inflammation, a risk factor for all types of cancer. Dietary inflammatory index (DII) was recently develpoed to evalute the inflammatory potential of a diet either as anti-inflammatory or pro-inflammatory. In fact, several studies have shown the association of DII and risk of different cancer types. The aim of this meta-analysis was to investigate the association of DII with risk of incidence and mortality of any cancer types.
Methods:
We searched PubMed-Medline, Scopus, and Web of Science databases for pertient studies util January, 2017. All studies conducted to investigate the association of DII and incidence, mortality, and hospitalization of all cancer types were included. According to degree of heterogeneity, fixed- or random-effect model was employed by STATA software.
Results:
Total 38 studies were eligible for the meta-analysis. The results show that a higher level of DII increases the risk for all cancer types incidence by 32% (OR: 1.32; 95% CI: 1.22-1.42) including digestive tract cancers (OR: 1.55; 95% CI: 1.33-1.78), hormone-dependent cancers (OR: 1.14; 95% CI: 1.04-1.24), respiratory tract cancers (OR: 1.64; 95% CI: 1.11-2.17), and urothelial cancers (OR: 1.36; 95% CI: 1.01-1.73). Moreover, a higher level of DII is in association with a higher risk for mortality caused by all types of cancer by 16% (OR: 1.16; 95% CI: 1.01-1.32). In addition, meta-regression analysis reveals that the design of study can have a significant effect on the association of DII and incidence of all cancer types (slope: 0.54; P= 0.05). The stratified meta-analysis shows that the association of DII and incidence of all cancer types in case-control studies (OR: 1.53; 95% CI: 1.36-1.71) were more prominent than cohort studies (OR: 1.18; 95% CI: 1.07-1.30).
Conclusions:
This study shows that a higher level of DII is associated with a higher risk of incidence and mortality of all cancer types. The findings of the present study suggest that modifying inflammatory properties of dietary patterns can reduce the risk of incidence and mortality of all cancer types.
Keywords: Cancer, diet, dietary inflammatory index, inflammation
Background
Inflammation is now widely known as an adaptive pathophysiological response underlying various chronic diseases including type 2 diabetes mellitus, cardiovascular disease, obesity, metabolic diseases, and specific types of cancer.[1,2,3] Several factors are associated with inflammation such as sex, age, and lifestyle. Lifestyle such as diet, physical activity, and smoking as malleable factors can reduce inflammation and thereby contributing to health.
Diet plays a contributing role in the regulation of inflammatory process. Various biomarkers have used to evaluate the association of nutrition and low-grade inflammatory status.[4] Consequently, it may be beneficial to identify dietary patterns related to their inflammatory properties.[5] Dietary inflammatory index (DII) is a new approach used to evaluate the inflammatory potential of a diet as either anti-inflammatory or pro-inflammatory.[6] In fact, some of the dietary patterns such as western pattern diet rich in red meat and refined grains is associated with a higher level of CRP, TNF- α, IL-1β, IL-2, and IL-6, which is often referred to as pro-inflammatory biomarkers. In contrast, there is an inverse association between Mediterranean diet including high amounts of fruits, whole grains, extra-virgin olive oil, and pro-inflammatory status.[7,8]
Nowadays, the inflammatory properties of diet and its role in preventing chronic diseases have attracted much attention from health sciences researchers. Although in recent years several studies have shown the association of DII and risk of different cancer types, the findings of these studies are heterogeneous according to the type of study and cancer. However, according to our knowledge, pooled estimate of association of DII and all cancers is unclear and have not been investigated yet by systematic review. The aim of this meta-analysis was to investigate the association of DII with risk of incidence and mortality of any cancer types.
Methods
To evaluate the maximum level of sensitivity, we simultaneously searched main international electronic data sources; PubMed and NLM Gateway (for MEDLINE), Institute of Scientific Information (ISI), and SCOPUS for studies until January, 2017. Further, a hand-search of all references included in the identified articles. We did not limit our research by the publication date and language.
Our strategy for searching relevant studies was using the following key words “Index-based dietary patterns,” “dietary inflammatory Index or DII,” and all related domains to neoplasm,” “cancer,” “Malignancy,” and “tumor”.
Any observational epidemiologic study, either cross-sectional, case-control, or cohort, which had used DII, and the estimation of a adjusted effect size measure [odds ratio (OR), relative risk (RR), and hazard ratio (HR)] and 95% confidence interval (CI) comparing level and score of the DII with respect to the risk of incidence, mortality, and length of hospitalization of all cancer types were eligible to include in this systematic review. We excluded all papers with duplicate entries. In case of multiple publications on the same population, only the largest study or the main source of data was included.
The quality of studies was assessed using the Newcastle-Ottawa scale designing for cohort and case-control studies. According to this scale, 9 points can be allocated to each study including four scores for selection, two scores for comparability, and three scores for assessment of outcomes. The process of quality assessment and data extraction was carried out independently by two research experts. Quality assessment agreement on quality assessment between raters was established using Cohen's kappa statistic. The Kappa statistic for agreement on quality assessment was 0.92, which shows perfect agreement. The discrepancy between the raters was resolved by an auditor. Data were extracted according to a checklist. The items on the checklist included (a) the number of citation; (b) demographic characteristics of population such as age, target population, and type of cancers; (c) methodological information of study such as study design, food assessment questionnaire, duration of follow-up, sample size, type of effect size measure (OR, RR, and HR), and adjusted covariates.
Statistical analysis
We examined the association of DII and cancers in terms of morbidity (incidence), mortality, and length of hospitalization. For meta-analysis, we classified cancers into four main categories: (a) digestive tract cancers; (b) hormone-dependent cancers; (c) respiratory tract cancers; and (d) urothelial cancers. However, for those studies that reported several adjusted models, we included only the multivariate model. Although in this systematic review we included all studies with reported DII as continuous (score) or categorical variable (tertile/quartile/quintile), we performed meta-analysis only for DII as categorical variable. In meta-analysis, risk of incidence and mortality of cancer in the highest level of DII (last tertile/quartile/quintile) was compared with lowest level of DII (last tertile/quartile/quintile). Although a number of studies have reported cancer subsites, meta-analysis have not performed according to subsites of cancer.[9,10,11,12,13,14] The meta-analysis on the association between DII and risk of cancer mortality has been conducted only for all cancer mortality. Because there was only one study on the association between length of hospitalization and DII, we did perform mate-analysis for the association of DII and length of hospitalization of cancer.
The results reported as adjusted effect size measure and 95% CI. The Chi-square based Q test and I square statistics used to assess the heterogeneity between studies. The results of Q test were statistically significant at P < 0.1. Because of severe heterogeneity among studies on the reported values, pooled estimate was estimated using random-effect meta-analysis model (using the Dersimonian and Laird method). The forest plot also was used to present the results of meta-analysis schematically. A random-effects meta-regression was performed using unrestricted maximum likelihood method to evaluate the association of estimated effect size measure and potential confounders such as design of study, type of cancer, food assessment questionnaire, and publication year. Potential publication bias was assessed using Egger's weighted regression tests, and the results of Egger's test were statistically significant at P < 0.1. The funnel plot also was used to present the results of publication bias schematically. “Trim and fill” method was used to adjust the analysis for the effects of publication bias. All statistical analysis was performed using STATA 11 software.
Ethical considerations
The protocol of study was approved by the ethical committee of Alborz University of Medical Science. All reviewed studies were properly cited. For more information about a certain study, we contacted the corresponding authors.
Results
The literature search strategy yielded a total of 575 publications. Further, 148 duplicated articles were excluded. After screening titles and abstracts, 345 irrelevant publications were excluded. Then, 82 remained articles and 6 retrieved articles through reference checking were carefully assessed and reviewed for eligibility; of which, 50 studies were excluded according to inclusion criteria. Finally, 38 studies met the inclusion criteria [Figure 1]. The main results of the selected articles were discussed in terms of incidence (n = 29), mortality (n = 7), both of them (n = 1), and length of hospitalization (n = 1) in patients with different types of cancers.
Figure 1.
Papers search and review flowchart for selection of primary studies
We found 30 articles (i.e. 20 case- controls and 10 cohorts) on the association of DII and incidence of different cancer types [Table 1]. Twenty-eight articles used food frequency questionnaire (FFQ), and the rest used 24 hour dietary recall (24HR) and dietary history questionnaire as dietary assessment instruments. The highest and lowest effect size measures (95% CI) were observed for esophageal squamous cell carcinoma (OR: 8.24; 95% CI: 2.03-33.47) and breast cancer (HR: 0.85; 95% CI: 0.52-1.41), respectively.
Table 1.
Association between DII and risk of cancer incidence
| Study number | First author (year) | Design | Follow- -up (years) | Food assessment questionnaire | Type/site of cancer | Total sample size (incident cases) | Groups | Type of effect size measure | Effect size measure (95% CI) | Covariates |
|---|---|---|---|---|---|---|---|---|---|---|
| 1 | Samuel O. Antwi (2016)[30] | Case-control | NA | 144 -item FFQ | Pancreatic cancer | 2573 (817) | Quintile 5 (>-0.03, 4.47) vs. Quintile 1 (-5.33,-3.07) | OR | 2.54 (1.87-3.46) | Age, sex, race, diabetes, BMI, pack-years of smoking, education |
| 2 | Young Ae Cho (2016)[9] | Case-control | NA | 106-item semi-quantitative FFQ | Colorectal cancer Colon cancer Proximal colon cancer Distal colon cancer Rectal cancer | 2769 (923) 2306 (460) 2011 (165) 2141 (295) 2290 (444) | Tertile 3 (≥2.30) vs. Tertile 1 (<0.30) | OR | 2.16 (1.71-2.73) 2.05 (1.53-2.74) 1.68 (1.08-2.61) 2.28 (1.61-3.21) 2.23 (1.66-3.00) | age, sex, BMI, education, family history of colorectal cancer, physical activity, and total calorie intake |
| 3-1 | Pierre-Antoine Dugue (2016)[31] | cohort | 21.3 | 121-item FFQ | Urothelial cell carcinoma | 41514 (379) | Quintile 5 vs. Quintile 1* | HR | 1.24 (0.90-1.70) | sex, country of birth, smoking, alcohol consumption, body mass index physical activity, education, and socioeconomic status |
| 3-2 | Pierre-Antoine Dugue (2016)[31] | cohort | 21.3 | 121-item FFQ | Urothelial cell carcinoma | 41514 (379) | Continuous DII (per one unit increment) | HR | 1.07 (0.97-1.19) | sex, country of birth, smoking, alcohol consumption, body mass index physical activity, education, and socio-economic status |
| 4 | Isabell Ge (2015)[32] | case-control | NA | 176-items FFQ | Breast cancer | 8300 (2887) | Quintile 5 (1.922, 5.504) vs. Quintile 1 (-4.604, -0.213) | OR | 1.01 (0.86-1.17) | age, study region, lifestyle confounders (total physical activity after 50 years, energy intake), breast cancer risk factors (age of menarche, number of pregnancies, breastfeeding history, induction of menopause, first-degree family history of breast cancer, history of benign breast disease, number of mammograms, hormone use) |
| 5 | Laurie Graffouille`re (2016)a[33] | cohort | 12.6 | 24 HR | Breast cancer | 3771 (158) | Quartile 4 vs.Quartile 1* | HR | 0.85 (0.52-1.41) | Age, sex, intervention group of the initial SU.VI.MAX trial, number of 24-h dietary records, BMI, height, physical activity, smoking status, educational level, energy intake, and family history in addition to menopausal status |
| Prostate cancer | 2771 (123) | 2.08 (1.06-4.09) | ||||||||
| vnon-prostate cancer (other cancers) | 6542 (278) | 1.34 (0.92-1.95) | ||||||||
| All cancers | 6542 (559) | 1.23 (0.94-1.62) | ||||||||
| 6 | A. M. Hodge (2016)[26] | cohort | 18 | 121-item FFQ | Lung cancer | 35,303 (403) | Quartile 4 (0.39,4.86) vs. Quartile 1 (-4.91,-2.15) | HR | 1.31 (0.91-1.89) | pack-years, years since quit smoking, smoking status, country of birth, education, BMI, alcohol intake, physical activity, sex, SEIFA quintile, energy (includes an interaction between smoking status and country of birth) |
| 7 | Yunxia Lu (2016)[34] | Case-control | NA | 63-item FFQ | Esophageal squamous cell carcinoma | 946 (158) | Quartile 4 (≥1.46) vs. Quartile 1 (<−1.04) | OR | 4.35 (2.24-8.43) | age, sex, energy, education, tobacco smoking, alcohol intake, and physical activity in addition to reflux, and Helicobacter pylori infection (for oesophageal adenocarcinoma and gastroesophageal junctional adenocarcinoma) |
| Esophageal adenocarcinoma | 987 (181) | 3.59 (1.87-6.89) | ||||||||
| Gastroesophageal junctional adenocarcinoma | 1061 (255) | 2.04 (1.24-3.36) | ||||||||
| Esophageal or gastroesophageal junction adenocarcinoma | 1242 (436) | 2.42 (1.57-3.73) | ||||||||
| 8 | Patrick Maisonneuve (2016)[35] | Cohort | 8.5 | 45-item FFQ | Lung cancer | 4336 (200) | Quartile 4 vs.Quartile 1* | HR | 1.54 (0.93-2.55) | baseline risk probability (age, sex, smoking duration, smoking intensity, years of smoking cessation, and asbestos exposure) and total energy |
| 9-1 | Lauren C. Peres (2017)[36] | case-control | NA | 110-item FFQ | Epithelial ovarian cancer | 1155 (493) | Quartile 4 (-0.32, 3.19) vs. Quartile 1 (-5.57, -3.64) | OR | 1.72 (1.18-2.51) | study design variables, age, and study site, family history of breast or ovarian cancer in a first degree relative, parity, OC use, education, BMI, tubal ligation, menopausal status, smoking status, and endometriosis |
| 9-2 | Lauren C.Peres (2017)[36] | case-control | NA | 110-item FFQ | Epithelial ovarian cancer | 1155 (493) | Continuous DII (per one unit increment) | OR | 1.10 (1.03-1.17) | study design variables, age and study site, family history of breast or ovarian cancer in a first degree relative, parity, OC use, education, BMI, tubal ligation, menopausal status, smoking status, and endometriosis |
| 10-1 | Nitin Shivappa (2016)a[37] | Cohort | 25 | 121-item FFQ | Breast cancer | 34700 (2934) | Tertile 3 (> -0.05) vs.Tertile 1 (<-2.08) | HR | 1.11 (1.00-1.22) | Age, energy and BMI, smoking status, pack-years of smoking, education, HRT use, oral contraceptive use, number of live births, education, age at menarche, age at menopause and history of hysterectomy |
| 10-2 | Nitin Shivappa (2016)a[37] | Cohort | 25 | 121-item FFQ | Breast cancer | 34700 (2934) | Continuous DII (per one unit increment) | HR | 1.01 (0.99-1.04) | Age, energy and BMI, smoking status, pack-years of smoking, education, HRT use, oral contraceptive use, number of live births, education, age at menarche, age at menopause and history of hysterectomy |
| 11 | Nitin Shivappa (2015)c[38] | case-control | NA | 78-item FFQ | Prostate cancer | 2754 (1294) | Quartile 4 (>0.49) vs. Quartile 1 (<-1.98) | OR | 1.33 (1.01-1.76) | Age, study center, BMI, years of education, social class, smoking status, family history of prostate cancer, and total energy intake |
| 12 | Nitin Shivappa (2015)d[39] | case-control | NA | 78-item FFQ | Pancreatic cancer | 978 (326) | Quintile 5 (≥ 1.27) vs.Quintile 1 (< - 1.28) | OR | 2·48 (1.50-4.10) | Age, sex, study center, year of interview, education, BMI, smoking status, alcohol drinking, and history of diabetes |
| 13-1 | Nitin Shivappa (2016)f[40] | case-control | NA | 78-item FFQ | Gastric cancer | 777 (230) | Quartile 4 (>1.49) vs. Quartile 1 (≤1.47) | OR | 2.35 (1.32-4.20) | study center, age, education, year of interview, BMI, smoking and total energy intake |
| 13-2 | Nitin Shivappa (2016)f[40] | case-control | NA | 78-item FFQ | Gastric cancer | 777 (230) | Continuous DII (per one unit increment) | OR | 1.19 (1.06-1.34) | study center, age, education, year of interview, BMI, smoking, and total energy intake |
| 14-1 | Nitin Shivappa (2015)g[41] | case-control | NA | 125-item FFQ | Esophageal squamous cell carcinoma | 143 (47) | High (>1.20) vs. Low (≤120) | OR | 8.24 (2.03-33.47) | age, energy, sex, BMI, years of education, physical activity, smoking, and gastro-oesophageal reflux |
| 14-2 | Nitin Shivappa (2015)g[41] | case-control | NA | 125-item FFQ | Esophageal squamous cell carcinoma | 143 (47) | Continuous DII (per one unit increment) | OR | 3.58 (1.76-7.26) | age, energy, sex, BMI, years of education, physical activity, smoking, and gastro-oesophageal reflux |
| 15-1 | Nitin Shivappa (2016)h[42] | case-control | NA | 78-item FFQ | Breast cancer | 5157 (2569) | Quintile 5 (1.28, 5.14) vs.Quintile 1 (-6.18,-2.13) | OR | 1.75 (1.39-2.21) | age, study center, and energy intake, education, body mass index, parity, menopausal status, and family history of hormone-related cancers |
| 15-2 | Nitin Shivappa (2016)h[42] | case-control | NA | 78-item FFQ | Breast cancer | 5157 (2569) | Continuous DII (per one unit increment) | OR | 1.09 (1.05-1.14) | age, study center, and energy intake, education, body mass index, parity, menopausal status, and family history of hormone-related cancers |
| 16-1 | Nitin Shivappa (2016)i[43] | case-control | NA | 80-item FFQ | Bladder Cancer | 1355 (690) | Quartile 4 (0.42, 4.58) vs.Quartile 1 (-5.94,-2.41) | OR | 1.97 (1.28-3.03) | age, sex, year of interview, study center, total energy intake, education, and tobacco smoking |
| 16-2 | Nitin Shivappa (2016)i[43] | case-control | NA | 80-item FFQ | Bladder Cancer | 1355 (690) | Continuous DII (per one unit increment) | OR | 1.11 (1.03-1.20) | age, sex, year of interview, study center, total energy intake, education, and tobacco smoking |
| 17-1 | Nitin Shivappa (2016)j[27] | case-control | NA | 78-item FFQ | ovarian cancer | 3442 (1031) | Quartile 4 (>1.35) vs. Quartile 1 (≤1.63) | OR | 1.47 (1.07-2.01) | age, energy intake, year of interview, study center, education, body mass index, parity, oral contraceptive use, menopausal status, and family history of ovarian and/or breast cancer in first-degree relatives |
| 17-2 | Nitin Shivappa (2016)j[27] | case-control | NA | 78-item FFQ | Ovarian cancer | 3442 (1031) | Continuous DII (per one unit increment) | OR | 1.08 (1.02-1.14) | age, energy intake, year of interview, study center, education, body mass index, parity, oral contraceptive use, menopausal status, and family history of ovarian and/or breast cancer in first-degree relatives |
| 18-1 | Nitin Shivappa (2016)k[44] | case-control | NA | 78-item FFQ | Laryngeal cancer | 1548 (460) | Quartile 4 (0.27, 5.00) vs.Quartile 1 (-5.48,-2.19) | OR | 3.30 (2.06-5.28) | age, sex, center, education, body mass index, tobacco smoking, alcohol consumption, and non-alcohol energy intake |
| 18-2 | Nitin Shivappa (2016)k[44] | case-control | NA | 78-item FFQ | Laryngeal cancer | 1548 (460) | Continuous DII (per one unit increment) | OR | 1.27 (1.15, 1.40) | age, sex, center, education, body mass index, tobacco smoking, alcohol consumption, and non-alcohol energy intake |
| 19-1 | Nitin Shivappa (2016)l[45] | case-control | NA | 78-item FFQ | Nasopharyngeal cancer | 792 (198) | Tertile 3 (men: >0.59; women: >−0.19) vs. Tertile1 (men: ≤−0.64; women: ≤−1.06) | OR | 1.64 (1.06-2.55) | place of living, sex, age, year of interview, education, smoking, alcohol drinking, and energy intake according to the residual method |
| 19-2 | Nitin Shivappa (2016)l[45] | case-control | NA | 78-item FFQ | Nasopharyngeal cancer | 792 (198) | Continuous DII (per one unit increment) | OR | 1.19 (1.05, 1.36) | place of living, sex, age, year of interview, education, smoking, alcohol drinking, and energy intake according to the residual method |
| 20-1 | Nitin Shivappa (2016)m[46] | case-control | NA | 78-item FFQ | Endometrial cancer | 1362 (454) | Quartile 4 ( >1·04) vs. Quartile 1 (<−1·07) | OR | 1·46 (1·02-2·11) | age, energy, year of interview, education, BMI, age at menarche, menopausal status and age at menopause, parity, history of diabetes, family history of cancers, oral contraceptive use and hormone replacement therapy use |
| 20-2 | Nitin Shivappa (2016)m[46] | case-control | NA | 78-item FFQ | Endometrial cancer | 1362 (454) | Continuous DII (per one unit increment) | OR | 1.07 (0.98-1.17) | age, energy, year of interview, education, BMI, age at menarche, menopausal status and age at menopause, parity, history of diabetes, family history of cancers, oral contraceptive use and hormone replacement therapy use |
| 21-1 | Nitin Shivappa (2015)n[47] | case-control | NA | 21-item FFQ | Prostate cancer | 479 (229) | Quartile 4 vs. Quartile 1* | OR | 2.39 (1.14-5.04) | age, BMI, smoking status, education, physical activity, energy intake, family history of prostate cancer |
| 21-2 | Nitin Shivappa (2015)n[47] | case-control | NA | 21-item FFQ | Prostate cancer | 479 (229) | Continuous DII (per one unit increment) | OR | 1.27 (0.98-1.50) | age, BMI, smoking status, education, physical activity, energy intake, and family history of prostate cancer |
| 22-1 | Nitin Shivappa (2014)o[10] | Cohort | 19.6±7.0 | 121-item FFQ | Colorectal cancer | 34703 (1636) | Quintile 5 (>1.10) vs. Quintile 1 (<-2.75) | HR | 1.20 (1.01-1.43) | age, BMI, smoking status, pack-years of smoking, HRT use, education, diabetes, and total energy intake |
| Colon cancer | 34703 (1329) | 1.19 (0.98-1.45) | ||||||||
| Rectal cancer | 34703 (325) | 1.21 (0.81-1.79) | ||||||||
| 22-2 | Nitin Shivappa (2014)o[10] | Cohort | 19.6±7.0 | 121-item FFQ | Colorectal cancer | 34703 (1636) | Continuous DII (per one unit increment) | HR | 1.07 (1.01-1.13) | age, BMI, smoking status, pack-years of smoking, HRT use, education, diabetes, and total energy intake |
| Colon cancer | 34703 (1329) | 1.05 (0.99-1.12) | ||||||||
| Rectal cancer | 34703 (325) | 1.11 (0.98-1.25) | ||||||||
| 23-1 | Nitin Shivappa (2015)p[48] | Cohort | 20 | 80-item FFQ | Breast cancer | 45257 (1895) | Quartile 4 (>3.77) vs. Quartile 1 (<1.87) | HR | 1.18 (1.00-1.39) | age, energy, age at first birth and number of children, age at menarche, BMI, height, multivitamin use, education, smoking status, oral contraceptive use, and family history of breast cancer in the model |
| 23-2 | Nitin Shivappa (2015)p[48] | Cohort | 20 | 80-item FFQ | Breast cancer | 45257 (1895) | Continuous DII (per one unit increment) | HR | 1.04 (1.01-1.09) | age, energy, age at first birth and number of children, age at menarche, BMI, height, multivitamin use, education, smoking status, oral contraceptive use, and family history of breast cancer in the model |
| 24-1 | Nitin Shivappa (2015)r [11] | case-control | NA | 78-item FFQ | Colorectal cancer | 6107 (1953) | Quintile 5 (>1.22) vs. Quintile 1 (≤ -1·05) | OR | 1.55 (1.29-1.85) | age, sex, study center, education, BMI, alcohol drinking, physical activity, and history of colorectal cancer and energy intake (using the residual method) |
| Colon cancer | 5379 (1225) | 1·39 (1.13-1.71) | ||||||||
| Rectal cancer | 4882 (728) | 1·47 (1.14-1.90) | ||||||||
| 24-2 | Nitin Shivappa (2015)r [11] | Case-control | NA | 78-item FFQ | Colorectal cancer | 6107 (1953) | Continuous DII (per one unit increment) | OR | 1·13 (1·09-1·18) | age, sex, study center, education, BMI, alcohol drinking, physical activity, and history of colorectal cancer and energy intake (using the residual method) |
| Colon cancer | 5379 (1225) | 1·09 (1·04, 1·14) | ||||||||
| Rectal cancer | 4882 (728) | 1·12 (1·06, 1·19) | ||||||||
| 25-1 | Nitin Shivappa (2015)s [49] | Case-control | NA | 78-item FFQ | Esophageal squamous cell cancer | 1047 (304) | Quintile 5 (>1.28) vs. Quintile 1 (<-1.20) | OR | 2.47 (1.40-4.36) | age, sex, year of interview, and area of residence and adjusted for education, alcohol drinking, tobacco smoking, BMI, physical activity, aspirin use, and energy (using the residual method) |
| 25-2 | Nitin Shivappa (2015)s [49] | Case-control | NA | 78-item FFQ | Esophageal squamous cell cancer | 1047 (304) | Continuous DII ( per one unit increment) | OR | 1.23 (1.10-1.38) | age, sex, year of interview, and area of residence and adjusted for education, alcohol drinking, tobacco smoking, BMI, physical activity, aspirin use, and energy (using the residual method) |
| 26 | Fred K Tabung (2016)a [50] | Cohort | 16.02 | 122-item FFQ | Breast cancer | 122788 (7495) | Quintile 5 (1.898,5.519) vs. Quintile 1 (-7.055,< -3.142) | HR | 0.99 (0.91-1.07) | age, energy intake, race/ethnicity, income, education, smoking status, mammography within 2 years of baseline, age at menarche, number of live births, oophorectomy status, hormone therapy use, nonsteroidal anti-inflammatory drug (NSAID) use, dietary modification trial arm, hormone therapy trial arm, body mass index, and physical activity |
| 27 | Fred K Tabung (2015)b [12] | Cohort | 11.3 | 122-item FFQ | Colorectal cancer | 152,536 (1920) | Quintile 5 (1.953, 5.636) vs. Quintile 1 (-7.055, < -3.136) | HR | 1.22 (1.05-1.43) | |
| Colon cancer | 152,536 (1559) | 1.23 (1.03-1.46) | ||||||||
| Proximal colon cancer | 152,536 (1034) | 1.35 (1.09-1.67) | ||||||||
| Distal colon cancer | 152,536 (428) | 0.84 (0.61-1.18) | ||||||||
| Rectal cancer | 152,536 (361) | 1.20 (0.84-1.72) | ||||||||
| 28-1 | Ruth A. Vázquez-Salas (2016)[51] | Case-control | NA | 127-item semi-quantitative FFQ | Prostate cancer | 1188 (394) | Tertile 1 (ref) (<−0·12) vs. Tertile 3 (≥1·28) Continuous DII (per…) | OR | 1.18 (0.85-1.63) | age, educational level, history of PC in first-degree relatives, BMI 2 years before the interview, physical activity throughout life, smoking status 5 years before the interview,history of chronic diseases |
| 28-2 | Ruth A. Vázquez-Salas (2016)[51] | Case-control | NA | 127-itemsemi - quantitative FFQ | Prostate cancer | 1188 (394) | Continuous DII (per one unit increment) | OR | 1·02 (0·94, 1·11) | age, educational level, history of PC in first-degree relatives, BMI 2 years before the interview, physical activity throughout life, smoking status 5 years before the interview, history of chronic diseases |
| 29-1 | Michael D. Wirth (2015)[13] | Cohort | 9.1±2.9 | 124-item FFQ | Colorectal cancer | 489,442 (6225) | Quartile 4 (3.25, 6.97) vs. Quartile 1 ( -7.33,-0.59) | HR | 1.40 (1.28-1.53) | age, smoking status, BMI, self-reported diabetes, and energy intake - for 1:physical activity, marital status, education and age (STRATA statement) - for 2:age (STRATA statement) - For 3:race and age - For 4:marital status, education, perceived health, census-based income and age (STRATA statement) - for 5:self-reported polyps, education, age and census-based income |
| Ascending/Cecum | 489,442 (2060) | 1.27 (1.09-1.49) | ||||||||
| Transverse/Hepatic and Splenic Flexure | 489,442 (802) | 1.58 (1.23-2.03) | ||||||||
| Descending/Sigmoid | 489,442 (1614) | 1.61 (1.35-1.91) | ||||||||
| Rectum/Recto sigmoid | 489,442 (1680) | 1.45 (1.22-1.73) | ||||||||
| 29-2 | Michael D. Wirth (2015)[13] | Cohort | 9.1±2.9 | 124-item FFQ | Colorectal cancer | 489,442 (6225) | Continuous DII ( per one unit increment) | HR | 1.06 (1.05-1.08) | age, smoking status, BMI, self-reported diabetes, and energy intake - for 1:physical activity, marital status, education and age (STRATA statement) - for 2:age (STRATA statement) - For 3:race and age - For 4:marital status, education, perceived health, census-based income and age (STRATA statement) -for 5:self-reported polyps, education, age and census-based income |
| Ascending/Cecum | 489,442 (2060) | 1.05 (1.02-1.07) | ||||||||
| Transverse/Hepatic and Splenic Flexure | 489,442 (802) | 1.06 (1.02-1.10) | ||||||||
| Descending/Sigmoid | 489,442 (1614) | 1.08 (1.05-1.11) | ||||||||
| Rectum/Recto sigmoid | 489,442 (1680) | 1.08 (1.05-1.10) | ||||||||
| 30-1 | Raul Zamora-Ros (2015)[14] | Case-control | NA | dietary history questionnaire | Colorectal cancer | 825 (424) | Quartile 4 (>3.05) vs. Quartile 1 (<-0.73) | OR | 1.65 (1.05-2.60) | sex, age, total energy intake, BMI, first-degree family history of colorectal cancer, physical activity, tobacco consumption, and medication use (aspirin and non-steroidal anti-inflammatory drug) |
| Colon cancer | 666 (265) | 2.24 (1.33-3.77) | ||||||||
| Rectal cancer | 560 (159) | 1.12 (0.61-2.06) | ||||||||
| 30-2 | Raul Zamora-Ros (2015)[14] | Case-control | NA | dietary history questionnaire | Colorectal cancer | 825 (424) | Continuous DII ( per one unit increment) | OR | 1.08 (1.01-1.15) | sex, age, total energy intake, BMI, first-degree family history of colorectal cancer, physical activity, tobacco consumption, and medication use (aspirin and non-steroidal anti-inflammatory drug |
| Colon cancer | 666 (265) | 1.12 (1.04-1.21) | ||||||||
| Rectal cancer | 560 (159) | 1.03 (0.95-1.12) |
Abbreviation: FFQ: food frequency questionnaire, 24HR: 24 hour recall, HR: hazard ratio, OR: odds ratio; DII: dietary inflammatory index; NA: not applicable
Table 2 summarizes 8 cohort studies on the association of DII and mortality of different cancer types. Dietary intake was measured using FFQ and 24HR in the five and three articles, respectively.
Table 2.
Association of DII and risk of cancer mortality
| Study number | First author (year) | design | Follow up (years) | Food assessment questionnaire | Study subjects | Type of cancer mortality | Total sample size (death number) | Groups | Type of effect size measure | Effect size measure (95% CI) | Covariates |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1-1 | Fang Emily Deng (2016)[52] | cohort | 135 and 168 person - months | 24 HR | Normal | Allcancers | 9631 (385) | Tertile 1 (ref) (<−0.20) vs. Tertile 3 (>2.0) | HR | 1.23 (0.84-1.79) | age, sex, race, HgbA1C, current smoking,physical activity, BMI, SBP |
| Lung cancer | 9631 (99) | 1.4 (0.79-2.47) | |||||||||
| Digestive-tract cancer | 9631 (99) | 1.38 (0.69-2.76) | |||||||||
| 1-2 | Fang Emily Deng (2016)[52] | cohort | 135 and 168 person - months | 24 HR | Pre - diabetic | All cancers | 2681 (208) | Tertile 1 (ref) (<−0.20) vs. Tertile 3 (>2.0) | HR | 2.02 (1.27-3.21) | age, sex, race, HgbA1C, current smoking, physical activity, BMI, SBP |
| Lung cancer | 2681 (66) | 2.01 (0.93-4.34) | |||||||||
| Digestive-tract cancer | 2681 (50) | 2.89 (1.08-7.71) | |||||||||
| 1-3 | Fang Emily Deng (2016)[52] | cohort | 135 and 168 person - months | 24 HR | Diabetic | All cancers | 968 (83) | Tertile 1 (ref) (<−0.20) vs. Tertile 3 (>2.0) | HR | 1.00 (0.49-2.04) | age, sex, race, HgbA1C, current smoking, physical activity, BMI, SBP |
| Lung cancer | 968 (27) | 0.55 (0.09-3.36) | |||||||||
| Digestive-tract cancer | 968 (27) | 1.30 (0.40-4.28) | |||||||||
| 2-1 | Aleksander Galas (2014)a[53] | cohort | 3,180.31 person - years | 148 item semi - quantitative FFQ | Patients without distant metastases | Colorectal cancer | 511 (150) | High (>- 2.27) vs. low (≤ -2.27) | HR | 0.76 (0.55-1.08) | Age, smoking, marital status, overweight or obesity, calendar year when surgery was performed, surgery type, cancer site, chemotherapy after surgery, radiotherapy after surgery |
| Patients with distant metastases | 178 (159) | 1.06 (0.76-1.48) | |||||||||
| 2-2 | Aleksander Galas (2014)a[53] | cohort | 3,180.31 person - years | 148 itemsemi - quantitative FFQ | Patients without distant metastases | Colorectal cancer | 511 (150) | Continuous DII (per one unit increment) | HR | 0.98 (0.92-1.05) | Age, smoking, marital status, overweight or obesity, calendar year when surgery was performed, surgery type, cancer site, chemotherapy after surgery, radiotherapy after surgery |
| Patients with distant metastases | 178 (159) | 1.003 (0.93-1.08) | |||||||||
| 3-1 | Laurie Graffouille`re (2016)b[54] | cohort | 12.4 | 24 HR | Healthy subjects | All cancers | 7994 (123) | Tertile 3 vs. Tertile 1* | HR | 1.83 (1.12-2.99) | Age, sex, intervention group of the initial SU.VI.MAX trial, number of 24-h dietary records, BMI, physical activity, smoking status, educational level, family history of cancer in first-degree relatives, family history of CVD in first-degree relatives, energy intake without alcohol, and alcohol intake |
| 3-2 | Laurie Graffouille`re (2016)b[54] | cohort | 12.4 | 24 HR | Healthy subjects | All cancers | 7994 (123) | Continuous DII (per one unit increment) | HR | 1.18 (1.04-1.34) | age, sex, intervention group of the initial SU.VI.MAX trial, number of 24-h dietary records, BMI, physical activity, smoking status, educational level, family history of cancer in first-degree relatives, family history of CVD in first-degree relatives, energy intake without alcohol, and alcohol intake |
| 4-1 | Nitin Shivappa (2016)b[55] | Cohort | 25 | 121-item FFQ | postmeno-pausal women | All cancers | 37525 (5044) | Quartile 4 (0.6469 to 4.6598) vs. Quartile 1(−5.7509 to−2.5041) | HR | 1.08 (0.99-1.18) | age, BMI, smoking status, pack-years of smoking, HRT use, education, prevalent diabetes, prevalent hypertension, prevalent heart disease, prevalent cancer, total energy intake |
| Digestive tract cancers | 37525 (1240) | 1.19 (1.00-1.43) | |||||||||
| 4-2 | Nitin Shivappa (2016)b[55] | Cohort | 25 | 121-item FFQ | postmeno-pausal women | All cancers | 37525 (5044) | Continuous DII (per one unit increment) | HR | 1.04 (1.01-1.07) | age, BMI, smoking status, pack-years of smoking, HRT use, education, prevalent diabetes, prevalent hypertension, prevalent heart disease,prevalent cancer, total energy intake |
| Digestive tract cancers | 37525 (1240) | 1.07 (1.01-1.14) | |||||||||
| 5-1 | Nitin Shivappa (2016)e[56] | Cohort | 15 | 96-item FFQ | Healthy women | All cancers | 33747 (1996) | Quintile 5 (> 5.10) vs. Quintile 1 (<−4.19) | HR | 1.25 (0.96-1.64) | Age, energy, BMI, education, smoking status, physical activity, alcohol intake |
| Digestive tract cancers | 33747 (602) | 1.42 (0.82-2.49) | |||||||||
| 5-2 | Nitin Shivappa (2016)e[56] | Cohort | 15 | 96-item FFQ | Healthy women | All cancers | 33747 (1996) | Continuous DII (per one unit increment) | HR | 1.04 (0.99-1.11) | Age, energy, BMI, education, smoking status, physical activity, alcohol intake |
| Digestive cancer | 33747 (602) | 1.15 (1.02-1.29) | |||||||||
| 6-1 | Nitin Shivappa (2015)q[57] | Cohort | 13.5±4.0 | 24 HR | Healthy subjects | All cancers | 12366 (615) | Tertile 3 (2.03 to 4.83) vs. Tertile 1 (−5.60 to−0.22) | HR | 1.46 (1.10-1.96) | age, sex, race, diabetes status, hypertension, physical activity, BMI, poverty index, and smoking |
| Digestive tract cancers | 12,366 (158) | ||||||||||
| 2.10 (1.15-3.84) | |||||||||||
| 6-2 | Nitin Shivappa (2015)q[57] | Cohort | 13.5±4.0 | 24 HR | Healthy subjects | All cancers | 12,366 (615) | Continuous DII (per one unit increment) | HR | 1.04 (0.97-1.11) | age, sex, race, diabetes status, hypertension, physical activity, BMI, poverty index, and smoking |
| Digestive tract cancers | |||||||||||
| 12,366 (158) | 1.08 (0.95-1.22) | ||||||||||
| 7 | Fred K Tabung (2016) a[50] | Cohort | 16.02 | 122-item FFQ | Postmeno-pausal women | Breast cancer | 122788 (667) | Quintile 5 (1.874 to 5.519) vs. Quintile 1 (−7.055 to <−3.162) | HR | 1.33 (1.01-1.76) | age, energy intake, race/ethnicity, income, education, smoking status, mammography within 2 years of baseline, age at menarche, number of live births, oophorectomy status, hormone therapy use, nonsteroidal anti-inflammatory drug (NSAID) use, dietary modification trial arm, hormone therapy trial arm, body mass index, and physical activity |
| 8 | Antonella Zucchetto (2016)[58] | Cohort | 12.7 | 78-item FFQ | Patients with prostate cancer | Prostate cancer | 726 (76) | Tertile 3 vs. Tertile 1* | HR | 1.42 (0.73-2.76) | area of residence, calendar period of diagnosis, age at diagnosis, education, smoking habits, abdominal obesity, alcohol intake, energy intake |
FFQ: Food frequency questionnaire, 24HR: 24 hour recall, HR: Hazard ratio, DII: Dietary inflammatory index
We found only one cohort study Table 3 on the association between DII and length of hospitalization. There was no significant association exists between DII and length of hospitalization in surgical patients treated for colorectal cancer.
Table 3.
Association between DII and length of hospitalization
| Study number | First author (year) | design | Follow up (years) | Food assessment questionnaire | Study subjects | Type of cancer mortality | Total sample size (death number) | Groups | Type of effect size measure | Effect size measure (95% CI) | Covariates |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | Aleksander Galas (2014)b[59] | Cohort | 11 days | 148 itemsemi - quantitative FFQ | Surgical patients treated for colorectal cancer | Colorectal cancer | 689 | Over the first tertile (> -3.41) vs. tertile 1 (≤ -3.41) | OR | 0.76 (0.53-1.09) | Age, smoking, marital status, overweight or obesity, calendar year when surgery was performed, surgery type, cancer site, chemotherapy after surgery, radiotherapy after surgery |
| Over the first quartile (> -3.91) vs. quartile 1 (≤ -3.91) | 0.69 (0.46-1.03) | ||||||||||
| Over the first quintile (> -4.25) vs. quintile 1 (≤ -4.25) | 0.69 (0.45-1.07) |
FFQ: Food frequency questionnaire, OR: Odds ratio
Table 4 presents the results of meta-analysis for the association of DII and incidence and mortality of different cancer types. There is a significant association between DII and incidence for all cancer types (OR: 1.32; 95% CI: 1.22-1.42; P < 0.001). A stratified meta-analysis by types of cancer shows that the highest and lowest effect size measures were observed for respiratory tract cancers and hormone-dependent cancers, respectively (OR: 1.64; 95% CI: 1.10-2.17 vs. OR: 1.14; 95% CI: 1.04-1.24). A stratified meta-analysis according to study design shows that the association of DII and incidence of all cancer types in case-control studies (OR: 1.53; 95% CI: 1.36-1.71) were more prominent than cohort studies (OR: 1.18; 95% CI: 1.07-1.30). Figures 2 and 3 report the forest plot of association between DII and cancer incidence according to the design of study and type of cancers, respectively. Moreover, there is a significant association between DII and mortality for all cancer types (HR: 1.16; 95% CI: 1.01-1.32) [Figure 4].
Table 4.
Meta-analysis of association between DII and mortality/morbidity of cancer
| Type of outcome (Mortality/morbidity) | subgroup | Type of cancer | Number of studies | Test of association | Test of heterogeneity | |||||
|---|---|---|---|---|---|---|---|---|---|---|
| Effect size measure | 95%CI | P | Model | I2 | Q test | P | ||||
| Morbidity | Type of cancer | Digestive tract cancers | 14 | 1.55 | 1.33-1.78 | < 0.001 | Random | 81.8 | 71.27 | < 0.001 |
| Hormone-dependent cancers | 13 | 1.14 | 1.04-1.24 | < 0.001 | Random | 59.6 | 29.72 | 0.003 | ||
| Respiratory tract cancers | 4 | 1.64 | 1.11-2.17 | < 0.001 | Fixed | 45.5 | 5.51 | 0.13 | ||
| Urothelial cancers | 2 | 1.36 | 1.00-1.73 | < 0.001 | Fixed | 54.8 | 2.21 | 0.13 | ||
| Type of study | Case-control | 22 | 1.53 | 1.36-1.71 | < 0.001 | Random | 77.3 | 92.51 | < 0.001 | |
| Cohort | 12 | 1.18 | 1.07-1.30 | < 0.001 | Random | 70.1 | 36.81 | < 0.001 | ||
| Overall | 34* | 1.32 | 1.22-1.42 | < 0.001 | Random | 74.5 | 129.39 | < 0.001 | ||
| Mortality | All cancers | 11 | 1.16 | 1.01-1.32 | < 0.001 | Random | 44.3 | 17.96 | 0.056 | |
*The sum of number of studies for all cancers (34 studies) is more than the sum of digestive, hormone-dependent, respiratory and urothelial cancers because in one study, type of cancer was not reported
Figure 2.
Odds ratio and 95% CI of individual studies and pooled data for the association between DII and incidence of cancer according to the type of study using random-effect model. OR: Odds of ratios
Figure 3.
Odds ratio and 95% CI of individual studies and pooled data for the association between DII and incidence of cancer according to the type of cancer using random-effect model. OR: Odds of ratios
Figure 4.
Odds ratio and 95% CI of individual studies and pooled data for the association between DII and mortality of cancer according to the type of cancer using random-effect model. OR: Odds of ratios
Meta-regression
A meta-regression analysis suggests that design of study can have a significant effect on the association between DII and cancer incidence (slope: 0.54; P = 0.05), whereas meta-regression does not show any significant associations between DII and type of food assessment questionnaire (slope:-0.33;p = 0.21), type of cancer (slope:-0.22; P = 0.22), and publication year (slope: 0.24;p = 0.31). The result of meta-regression analysis for the association of DII and cancer mortality shows no significant association between DII and type of food assessment questionnaire (slope: 0.43; P = 0.47), type of cancer (slope:-0.54; P = 0.81), and publication year (slope: 0.21; P = 0.59).
Publication bias
The results of Egger test for association of DII and all cancer incidence show that publication bias exists (coefficient: 2.87; P < 0.001) and funnel plot was asymmetric [Figure 5]. “Trim and fill” correction suggested some potentially missing study on the right side of funnel plot [Figure 5]. Imputation for this potentially missing study yielded an effect size of 1.23 (95% CI: 1.12-1.33). In addition, the results of Egger test for association between DII and all cancer mortality show that publication bias does not exist (coefficient: 1.06; P = 0.15) and funnel plot was symmetric [Figure 6].
Figure 5.
Funnel plot detailing publication bias in the studies reporting the association between DII and all cancer morbidity
Figure 6.
Funnel plot detailing publication bias in the studies reporting the association between DII and cancer mortality
Discussion
To the best of our knowledge, the present study is the first comprehensive systematic review and meta-analysis on the association of DII and cancer incidence and mortality. This meta-analysis shows a significant association between DII and risk of incidence and mortality of all cancer types. The results of the present study shows that a higher level of DII increases the risk of cancers incidence by 32% (95% CI: 1.22-1.42) including digestive tract cancers (OR: 1.55; 95% CI: 1.33-1.78), hormone-dependent cancers (OR = 1.14; 95% CI: 1.04-1.24), respiratory tract cancers (OR: 1.64; 95% CI: 1.11-2.17), and urothelial cancers (OR: 1.36; 95% CI: 1.00-1.73). Moreover, a higher level of DII in association with a higher risk of mortality caused by all type of cancer by 16% (95% CI: 1.01-1.32).
Our findings were consistent with previous studies showing that a higher DII was associated with mortality. Moreover, some studies have documented a direct association between DII and a higher risk for metabolic syndrome and cardiovascular diseases (CVD).[4] One of the study reported different mechanisms by which inflammatory markers used for DII calculation can predict most prevalent diseases including cancers, CVD, and diabetes.[6]
Results of present study show that the association of DII and incidence of all cancer types in case-control studies were more prominent than cohort studies, which was consistent with previous studies.[15,16] It has been suggested that dietary recall bias may justify the discrepant results between case-control and cohort studies on diet and the risk of cancers.
Dietary patterns analysis is one of the most appropriate approaches to understand the relationship between diet and risk for various diseases including diabetes, cancers, and CVD.[17] All of the healthy dietary patterns (e.g. Dietary Approaches to Stop Hypertension and Mediterranean diet) can play a key role in preventing major chronic diseases, especially cancers.[18,19,20] In contrast, there was an inverse relationship between DII and dietary quality indices (e.g. Healthy Eating Index).[21] This was in line with the number of studies showing an inverse correlation between C-reactive protein, one of the inflammatory biomarkers used to calculate the DII, and higher consumption of vegetables, fruits,[22] legumes,[23] and nuts.[24]
To define the inflammatory capacity of diet as a main determining factor for vast majority of chronic diseases, we developed DII from peer-reviewed literature by investigating the association between dietary components and inflammation. However, in contrast to the other dietary patterns, DII focuses on specific biological pathways modulating the impact of dietary factors on inflammation.[21] In fact, in comparison to other dietary pattern, DII can provide more comprehensive information on additional variables affecting inflammation.[25,26,27,28,29]
The present meta-analysis has some strengths and limitations. The main strength is that the study includes all indices of incidence, mortality, and length of hospitalization of cancers in relation with a categorical and continuous score of DII. In addition, we carried out the meta-analysis on all types of cancer. The limitations of the study were as follows: (a) reviewed studies were heterogeneous in terms of population characteristics, design, and duration of follow-up periods; and (b) the questionnaires used for food assessment were different. However, we tried to reduce the effect of heterogeneity on estimated effect sizes by using a random-effect model of analysis.
Conclusions
In conclusion, the present meta-analysis suggested a significant association between DII and incidence, mortality, and hospitalization in patients with different types of cancers. DII, which is used for evaluating inflammatory properties of diets, can be used as an appropriate tool to predict the incidence and mortality of all cancer types. According to the results of the study, we recommend that changing dietary patterns as malleable factors can substantially reduce both incidence and mortality risks in cancer patients.
Financial support and sponsorship
The study was funded by Alborz University of Medical Sciences.
Conflicts of interest
There are no conflicts of interest.
Acknowledgments
We would like to appreciate Emam Ali Hospital Clinical Research Development Unit, Alborz University of Medical Sciences for their comprehensive cooperation in this study.
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