Open Access

Dietary inflammatory index and inflammatory gene interactions in relation to colorectal cancer risk in the Bellvitge colorectal cancer case–control study

  • Raul Zamora-Ros1,
  • Nitin Shivappa2, 3,
  • Susan E. Steck2, 3,
  • Federico Canzian4,
  • Stefano Landi5,
  • M. Henar Alonso6, 7,
  • James R. Hébert2, 3 and
  • Victor Moreno6, 7, 8Email author
Genes & NutritionStudying the relationship between genetics and nutrition in the improvement of human health201410:447

https://doi.org/10.1007/s12263-014-0447-x

Received: 3 October 2014

Accepted: 26 November 2014

Published: 9 December 2014

Abstract

Chronic inflammation is an important factor in colorectal carcinogenesis. However, evidence on the effect of pro-inflammatory and anti-inflammatory foods and nutrients is scarce. Moreover, there are few studies focusing on diet–gene interactions on inflammation and colorectal cancer (CRC). This study was designed to investigate the association between the novel dietary inflammatory index (DII) and CRC and its potential interaction with polymorphisms in inflammatory genes. Data from the Bellvitge Colorectal Cancer Study, a case–control study (424 cases with incident colorectal cancer and 401 hospital-based controls), were used. The DII score for each participant was obtained by multiplying intakes of dietary components from a validated dietary history questionnaire by literature-based dietary inflammatory weights that reflected the inflammatory potential of components. Data from four important single nucleotide polymorphisms located in genes thought to be important in inflammation-associated CRC: i.e., interleukin (IL)-4, IL-6, IL-8, and peroxisome proliferator-activated receptor-γ (PPARG) were analyzed. A direct association was observed between DII score and CRC risk (ORQ4 vs. Q1 1.65, 95 % CI 1.05–2.60, and P trend 0.011). A stronger association was found with colon cancer risk (ORQ4 vs. Q1 2.24, 95 % CI 1.33–3.77, and P trend 0.002) than rectal cancer risk (ORQ4 vs. Q1 1.12, 95 % CI 0.61–2.06, and P trend 0.37). DII score was inversely correlated with SNP rs2243250 in IL-4 among controls, and an interaction was observed with CRC risk. Neither correlation nor interaction was detected for other inflammatory genes. Overall, high-DII diets are associated with increased risk of CRC, particularly for colon cancer, suggesting that dietary-mediated inflammation plays an important role in colorectal carcinogenesis.

Keywords

Dietary inflammatory indexColorectal cancerInflammatory genesCase–control study

Introduction

Colorectal cancer (CRC) is the third most frequently occurring cancer and the fourth most common cause of death from cancer worldwide (Ferlay et al. 2013). A considerable body of evidence suggests that inflammation plays a key role in the pathogenesis of CRC by stimulating angiogenesis, damaging DNA, and chronically stimulating cell proliferation (Coussens and Werb 2002). Thus, patients with a history of chronic inflammatory bowel diseases have an increased risk of developing CRC (Laukoetter et al. 2011), whereas habitual use of nonsteroidal anti-inflammatory drugs is associated with a lower CRC risk (Wang and DuBois 2013). Moreover, circulating inflammatory biomarkers, such as C-reactive protein (CRP) (Aleksandrova et al. 2010), cytokines, chemokines, and cell adhesion molecules (McClellan et al. 2012; Song et al. 2013), and some genes related to pro-inflammation (Landi et al. 2003, 2007) tended to be associated with a higher CRC risk. Diet plays a crucial role in the etiology of CRC (World Research Cancer Fund and American Institute for Cancer Research 2007), although there is little evidence of the pro-inflammatory and anti-inflammatory effects of the overall diet on CRC risk (Shivappa et al. 2014a).

The dietary inflammation index (DII) is a literature review-based score that reflects the potential inflammatory effects of the diet. It was developed by Cavicchia et al. (2009) and updated by Shivappa et al. (2014b). In this new version, nearly 2,000 papers were reviewed and scored. Forty-five food parameters, including foods, nutrients, and other bioactive compounds were evaluated based on their inflammatory effect on some specific inflammatory markers, such as interleukin (IL)-1, IL-4, IL-6, IL-10, tumor necrosis factor (TNF)-α, and CRP. The DII has been validated demonstrating its effectiveness in predicting serum CRP levels in a large longitudinal epidemiological study (Shivappa et al. 2014c). Previously, we observed that women with a pro-inflammatory diet (higher DII scores) had a higher risk of developing CRC in a US-based cohort study (Shivappa et al. 2014a). Furthermore, higher DII scores have been linked to asthma (Wood et al. 2014), and using a modification of the previous DII version (Hebert et al. 2014), a positive association was observed between the DII and higher concentrations of glucose metabolism markers (van Woudenbergh et al. 2013).

The aim of the current study was to investigate the association between DII and CRC risk, and the potential interactions with some polymorphisms of inflammatory genes in a Spanish case–control study.

Subjects and methods

Study design and case ascertainment

The Bellvitge Colorectal Cancer Study is a hospital-based case–control study designed to investigate the relationships between risk factors of CRC and gene–environment interactions. The full rationale, methods, and design have been described previously (Landi et al. 2003). Briefly, primary CRC cases were recruited at the University Hospital of Bellvitge, Barcelona (Spain), between January 1996 and December 1998. A total of 523 histologically confirmed CRC cases were identified, of whom 424 participated in the study (81 % participation rate). Controls were randomly selected from admissions to the same hospital during this period. To minimize selection bias, the criterion of inclusion in the control group was a new disease (not previously diagnosed) for that patient. Twenty-two percent of controls were admitted for internal medicine, 19 % for acute surgery, 17 % for urology, 16 % for gastroenterology (hernia, peptic ulcer, and cholecystitis), 15 % for traumatology, and 11 % for circulatory or respiratory conditions. Controls were frequency-matched to cases by sex and age (±5 years). A total of 470 controls were approached, of whom 442 were deemed eligible and 401 agreed to participate in the study (85 % participation rate). All participants gave written consent, all procedures were in accordance with the Ethical standards of the Helsinki Declaration, and the Ethical Committee of the hospital approved the study protocol.

Dietary assessment

The participants’ habitual diet in the year previous to diagnosis was recorded in a personal interview using a validated Spanish dietary history questionnaire (EPIC Group of Spain 1997a, b). Energy, nutrient, and flavonoid intakes were estimated from the Spanish food composition tables used for the European Prospective Investigation into Cancer and Nutrition study (Slimani et al. 2007; Zamora-Ros et al. 2013a, b). Questionnaire-derived dietary information was used to calculate DII scores for all subjects, as described in detail elsewhere (Cavicchia et al. 2009; Shivappa et al. 2014b). Briefly, the dietary data for each study participant were first linked to the regionally representative global database that provided a robust estimate of a mean and standard deviation for each of the food parameters (i.e., foods, nutrients, and other food components such as flavonoids) considered (Shivappa et al. 2014b) to derive a z-score, by subtracting the “standard global mean” from the amount reported and dividing this value by the standard deviation. To minimize the effect of “right skewing” (a common occurrence with dietary data), this value was then converted to a centered percentile score which was then multiplied by the respective food parameter effect score (derived from a literature review and scoring of 1,941 articles) to obtain subject’s food parameter-specific DII score. All of the food parameter-specific DII scores were then summed to create the overall DII score for every subject in the study (Supplementary Table 1). A positive score indicates a more pro-inflammatory diet, while a negative score reflects a diet that is more anti-inflammatory.

Gene and lifestyle assessment

Cases and controls were interviewed by trained personnel using structured questionnaires designed to collect information on sociodemographic characteristics, medical history, lifetime smoking habits, leisure- and work-related physical activity. Anthropometric data were measured, and a blood sample was taken.

The four selected SNPs of inflammatory genes [IL-4, IL-6, IL-8, and peroxisome proliferator-activated receptor-γ (PPARG)] were the genes significantly associated with CRC risk in our previous studies in the main effects or in the subgroup analyses (Landi et al. 2003, 2007). After DNA was extracted, genotyping was performed with the TaqMan technology using the protocol recommended by the supplier (Applied Biosystems, Foster City, CA, USA). The order of DNAs from cases and controls was randomized on PCR plates in order to ensure that a similar number of cases and controls were analyzed simultaneously in the same plate. Reactions were run in 96-well plates on a Tetrad DNA Engine PCR machine (MJ Research, Waltham, MA, USA) and read in a TaqMan 7900HT sequence detection system (Applied Biosystems, Foster City, CA, USA).

Statistical analysis

Characteristics of cases and controls were summarized as percentages of subjects for categorical variables and means and standard deviations for continuous variables. Distribution of DII score was assessed by the median (25th and 75th percentiles), because the data were skewed to the right.

The relationships between CRC risk and DII were assessed by estimating the odds ratios (OR) and 95 % confidence intervals (CIs) using an unconditional logistic regression, because the controls were frequency-matched to cases. DII score was included in the models as quartiles (categorically) based on the distributions among controls. To account for potential confounding and adjust for slight differences in the distribution of sex between cases and controls (Table 1), model 1 was adjusted for sex, age (years, continuous), and total energy intake (kcal/day, continuous). Model 2 was additionally adjusted for body mass index (kg/m2, continuous), tobacco consumption (former, current, and never smoker), level of physical activity (no activity, low, and high), regular medications (aspirin, nonsteroidal anti-inflammatory drug, both, and none), and first-degree family history of CRC (yes, no). Tests for linear trend were performed by assigning the medians of each quartile as scores. DII score was also analyzed as a continuous variable (one unit of DII increment). The primary analysis was performed for all CRC combined; secondary analyses were carried out for colon and rectal cancers separately. The Wald test was used to evaluate the association and heterogeneity between cancer sites.
Table 1

Characteristics of 424 colorectal cancer cases and 401 controls by quartiles of dietary inflammation index score in the Bellvitge Colorectal Cancer Study

 

All

Q1

Q2

Q3

Q4

Cutoff

 

<−0.73

−0.73 to 1.06

1.07 to 3.05

>3.05

N

 Cases

424

112

81

114

117

 Controls

401

101

101

98

101

Age (years)a

 Cases

66.2 (11.7)

63.8 (10.7)

65.1 (11.5)

67.5 (10.6)

68.0 (13.2)

 Controls

65.1 (12.5)

64.1 (11.0)

63.8 (11.9)

64.2 (12.8)

68.2 (13.7)

Men (%)

 Cases

60.1

80.4

66.7

57.0

39.3

 Controls

51.6

66.3

59.4

49.0

31.7

BMI (kg/m2)a

 Cases

25.9 (4.2)

26.5 (3.9)

25.7 (3.8)

26.0 (4.4)

25.3 (4.6)

 Controls

27.0 (4.8)

26.9 (4.9)

27.9 (4.3)

27.1 (5.4)

26.0 (4.6)

Current smokers (%)

 Cases

17.7

22.3

22.2

14.9

12.8

 Controls

14.7

11.9

17.8

16.3

12.9

High physical activity (%)

 Cases

51.3

52.7

56.3

52.6

45.3

 Controls

51.4

52.5

57.4

48.0

47.5

History of colorectal cancerb (%)

 Cases

15.3

17.9

13.6

13.2

16.2

 Controls

5.0

8.9

3.0

5.1

3.0

Energy (kcal/day)a

 Cases

2,175 (793)

2,713 (860)

2,317 (743)

1,983 (618)

1,748 (563)

 Controls

1,969 (678)

2,405 (725)

1,972 (594)

1,929 (630)

1,569 (469)

Alcohol (g/day)a

 Cases

12.0 (37.6)

31.8 (64.9)

7.6 (25.3)

15.0 (38.4)

2.1 (14.2)

 Controls

9.5 (36.5)

14.4 (49.2)

12.7 (41.7)

8.4 (35.5)

4.9 (20.3)

Fruit and vegetables (g/1,000 kcal day)a

 Cases

230 (134)

302 (145)

240 (135)

228 (125)

157 (86)

 Controls

274 (160)

364 (174)

299 (154)

245 (134)

190 (119)

Red and processed meat (g/1,000 kcal day)a

 Cases

38.3 (21.6)

38.5 (21.2)

42.0 (23.8)

36.5 (21.5)

37.1 (20.4)

 Controls

39.5 (22.8)

38.0 (24.8)

42.1 (24.4)

38.9 (19.5)

38.9 (22.3)

Aspirin (%)

 Cases

16.3

18.8

17.3

15.8

13.7

 Controls

18.7

13.9

18.8

15.3

26.7

NSAID (%)

 Cases

5.7

3.6

6.2

5.3

7.7

 Controls

14.5

10.9

19.8

12.2

14.9

aMean (SD)

bFirst-degree family history of colorectal cancer

Diet–gene associations were tested by investigating the relationships between DII score (continuous) and CRC risk by SNPs of inflammatory genes using unconditional logistic regression and adjusting for the same variables as in model 2. Association between gene polymorphisms and DII score was assessed only in the control population using a linear regression model adjusted for covariate as model 2 before to test if genotype frequencies followed Hardy–Weinberg equilibrium. This association was also assessed in the complete sample. Partial Pearson correlation coefficients for DII and polymorphisms were derived from the linear models. Interactions between DII score and gene polymorphisms in relation to CRC risk were tested using the likelihood ratio test from logistic regression models with and without the interaction terms. Case-only analysis, though more powerful, was not considered because DII score was associated with some polymorphisms among controls. We used Bonferroni correction to account for multiple test and used a P value of 0.0125 (0.05–4) to indicate statistical significance. All statistical tests were two-tailed and were performed using the SPSS package program, version 17.0 (SPSS, Chicago, IL) and the genetic epidemiology web tool SNPstats (http://www.snpstats.net) (Sole et al. 2006).

Results

A total of 424 CRC patients (265 and 159 with colon and rectal cancer, respectively) and 401 hospital-based control subjects were included in the current study. The medians (25th and 75th percentiles) of DII score were 1.44 (−0.88 and 3.18) and 1.06 (−0.73 and 3.05) for cases and controls, respectively. Age at recruitment and percentage of women were higher in the fourth quartile compared with the first (Table 1). In addition, subjects in the highest quartile tended to smoke less, particularly in cases, and to be less physically active. Furthermore, participants in the top quartile reported the lowest intake of total energy, alcohol, and fruit and vegetables (per 1,000 kcal).

In both multivariable logistic models, significant direct associations were observed between DII score and CRC risk (ORQ4 vs. Q1 1.65, 95 % CI 1.05–2.60, and P trend 0.011) and colon cancer risk (ORQ4 vs. Q1 2.24, 95 % CI 1.33–3.77, and P trend 0.002), but not with rectal cancer risk (ORQ4 vs. Q1 1.12, 95 % CI 0.61–2.06, and P trend 0.37) (Table 2). Similar results were found when DII score was evaluated as a continuous variable. However, no significant heterogeneity between colon and rectal cancer risk was detected (P heterogeneity = 0.19).
Table 2

Association between dietary inflammation index (DII) score and risk of colorectal cancer in the Bellvitge Colorectal Cancer Study

 

Cutoff

Colorectal cancer

Colon cancer

Rectal cancer

Cases

OR (95 % CI)a

OR (95 % CI)b

Cases

OR (95 % CI)a

OR (95 % CI)b

Cases

OR (95 % CI)a

OR (95 % CI)b

Q1

<−0.73

112

1

1

66

1

1

46

1

1

Q2

−0.73 to 1.06

81

0.89 (0.59–1.35)

0.96 (0.63–1.48)

55

1.09 (0.68–1.76)

1.25 (0.76–2.06)

26

0.65 (0.37–1.16)

0.71 (0.39–1.29)

Q3

1.07–3.05

114

1.44 (0.95–2.16)

1.51 (0.99–2.31)

66

1.47 (0.91–2.37)

1.58 (0.96–2.59)

48

1.36 (0.81–2.29)

1.48 (0.85–2.55)

Q4

>3.05

117

1.66 (1.08–2.56)

1.65 (1.05–2.60)

78

2.02 (1.23–3.33)

2.24 (1.33–3.77)

39

1.20 (0.67–2.14)

1.12 (0.61–2.06)

P trend

 

0.008

0.011

 

0.004

0.002

 

0.25

0.37

Continuous (1 DII unit)

424

1.08 (1.02–1.15)

1.08 (1.01–1.15)

265

1.10 (1.03–1.19)

1.12 (1.04–1.21)

159

1.04 (0.96–1.13)

1.03 (0.95–1.12)

aModel 1 was adjusted for sex, age, and total energy intake

bModel 2 was additionally adjusted for body mass index, first-degree family history of colorectal cancer, physical activity, tobacco consumption, and medication use (aspirin and nonsteroidal anti-inflammatory drug)

In the diet–gene analysis, a significant correlation between DII score and IL-4 rs2243250 polymorphism (partial r = −0.34, P = 0.009) was found among the control group. No significant correlation were observed with IL-6 (partial r = 0.20, P = 0.06), IL-8 (partial r = 0.15, P = 0.18), and PPARG (partial r = −0.06, P = 0.63) polymorphisms. Similar associations were observed in the complete dataset. A significant interaction was observed between DII score and IL-4 genotype in relation to CRC risk. Multivariable logistic models evaluating the association between DII score and CRC risk stratified by SNP of inflammatory genes are presented in Table 3. The DII score was not associated with CRC for individuals homozygous CC for rs2243250 in IL-4, but the DII score was associated with a significant increased risk of carriers of the T allele (dominant model) (OR 1.34, 95 % CI 1.14–1.57). No significant interaction was observed for IL-6, IL-8, or PPARG.
Table 3

Associations between dietary inflammatory index score (continuous) and colorectal cancer risk by polymorphisms in inflammatory genes in the Bellvitge Colorectal Cancer Study

Gene

Rs number

Trivial name

Cases

Controls

OR (95 % CI)a

P for interactionb

IL4 c

rs2243250

−588 C>T

   

0.004

 CC

  

209

207

1.04 (0.95–1.13)

 

 CT+TT

  

65

59

1.34 (1.14–1.57)

 

IL6 c

rs1800795

174 G>C

   

0.26

 GG

  

131

143

1.12 (1.01–1.24)

 

 GC+CC

  

222

163

1.04 (0.96–1.13)

 

IL8 c

rs4073

−251 T>A

   

0.85

 TT

  

114

81

1.11 (0.98–1.25)

 

 TA+AA

  

230

222

1.09 (1.01–1.19)

 

PPARG c

rs1801282

34 C>G

   

0.49

 CC

  

305

238

1.09 (1.01–1.17)

 

 CG+GG

  

46

65

1.03 (0.88–1.20)

 

IL interleukin, PPARG peroxisome proliferator-activated receptor-γ

aIncrease in CRC risk for each unit of DII score. Adjusted for sex, age, total energy intake, body mass index, first-degree family history of colorectal cancer, physical activity, tobacco consumption, and medication use (aspirin and nonsteroidal anti-inflammatory drug)

bDifferences in risk associated to dietary inflammatory index score by genotype

cNot available data for some individuals produce OR estimates different from the complete dataset shown in Table 2 (OR 1.08, 95 % CI 1.01–1.15). Missing values for IL-4: 150 cases and 135 controls, for IL-6: 71 cases and 95 controls, for IL-8: 80 cases and 98 controls, and for PPARG: 72 cases and 98 controls

Discussion

In the present case–control study, a statistically significant direct association was observed between CRC risk and DII score in a dose-dependent manner. CRC risk was increased by 51 and 65 % when participants in the third and the fourth DII quartile, respectively, were compared with those in the first quartile. Similar results were previously observed in the Iowa Women’s Health Study, although the CRC risk, in this cohort, was only increased by 20 % (Shivappa et al. 2014a). Despite the limited evidence on the relationship between overall inflammatory effects of diet and CRC risk, other epidemiological studies have reported comparable associations between CRC risk and anti-inflammatory foods (e.g., fruits and vegetables) (World Research Cancer Fund and American Institute for Cancer Research 2007), nutrients (e.g., fiber, selenium, and folate) (van Duijnhoven et al. 2009), and other bioactive compounds (e.g., flavonoids) (Zamora-Ros et al. 2013b). In addition, higher circulating CRP and cytokine levels (inflammatory markers) have been associated with increased CRC risk in case–control studies, but in cohort studies, these associations have been less conclusive (Aleksandrova et al. 2010; Song et al. 2013; Wu et al. 2013).

Our results suggest that the association of DII score with colon cancer risk could be stronger than with rectal cancer risk, but the interaction was not statistically significant. In other epidemiological studies, similar associations were reported for colon and rectal cancer risks with DII score (Shivappa et al. 2014a), and intakes of fruits and vegetables, fiber, and flavonoids (World Research Cancer Fund and American Institute for Cancer Research 2007; Zamora-Ros et al. 2013b; Murphy et al. 2012). However, for circulating CRP levels, significant associations were observed only for colon cancer risk (Aleksandrova et al. 2010; Wu et al. 2013). Although colon and rectal cancers may have different etiologies (Wei et al. 2004), our study did not show large differences between colon and rectal cancer in the effect of inflammation. Lack of statistically significant findings for rectal cancer may be a result of smaller sample size for rectal cancer.

In previous reports from our case–control study, SNPs in the IL-4, IL-6, IL-8, and PPARG genes related to inflammation pathways were associated with CRC risk (8; 9). For IL-4, the main effect was not statistically significant in the complete dataset (OR 1.23, 95 % CI 0.81–1.86), but was significant in the colon cancer subgroup (Landi et al. 2007). Our results have shown that individuals with a T allele in the rs2243250 SNP, located in the promoter region of IL-4, tended to have a lower DII. However, when these individuals had a high DII, their risk of CRC was significantly increased. This effect was not observed in individuals with the more frequent C allele. It is interesting that this SNP has been associated with diverse diseases related to inflammation, including cancer. In some studies, the T allele of this SNP was related to an increased disease risk, such as liver diseases (Zheng et al. 2013), renal cell cancer (Zhenzhen et al. 2013), and asthma (Liu et al. 2012). On the other hand, the T allele of this SNP was associated with a decrease risk in oral cancer (Zhenzhen et al. 2013) and myocardial infarct in young people (Paffen et al. 2008). We hypothesize that, in individuals with the T allele, the activity of the cytokine IL-4 may be downregulated. In diets with a low DII, protective effects of the IL-4 pathway might be compensated by other anti-carcinogenic and anti-inflammatory pathways. However, in diets with a high DII, these alternative pathways may be not enough, and therefore, the CRC risk was higher than in subjects with the C allele of this SNP. Further studies evaluating the association of inflammatory markers and CRC risk by SNP in inflammatory genes are needed to confirm our findings.

The first step of colorectal carcinogenesis occurs in an inflammatory environment wherein infiltrating lymphocytes and macrophages raise the level of reactive oxygen and nitrogen spices and stimulate release of pro-inflammatory grown factors, cytokines, and chemokines (Coussens and Werb 2002). Microbiota (Candela et al. 2014) and a healthy diet (Wang et al. 2012) play an important role in keeping intestinal mucosa in a state of low-grade inflammation. However, when this is chronically activated, the inflammatory/oxidative environment becomes a relentless cycle that results in genetic and pathological damage. All food components included in the DII score have been inversely or positively associated with inflammation (Shivappa et al. 2014b), and therefore, their inclusion in a dietary score is crucial to properly evaluate the complex association between diet-related inflammation and CRC risk.

We are aware that in any case–control study, there are potential limitations such as reverse-causality and that the use of hospital controls is not ideal, though there is some evidence that hospital controls may be superior to population controls (especially, when the base population is difficult to delineate) (Infante-Rivard 2003). Firstly, we tried to minimize measurement error by using validated questionnaires administered by trained interviewers (EPIC Group of Spain 1997a, b). Despite that, intakes of some dietary components, which were included in the previously published DII (Shivappa et al. 2014b), such as caffeine, eugenol, ginger, saffron, selenium, pepper, thyme,oregano, and rosemary, could not be calculated from our dietary history. However, the variation in intakes of those specific dietary components was expected to be low in a mostly non-vegetarian Spanish population. Secondly, although extensive information about potential confounders was available, residual confounding might have remained because potential confounders could have been measured with error. Thirdly, the use of hospital controls may have resulted in a selected control group with potentially different prevalence of inflammatory alleles than the reference population. However, it has previously shown that hospital controls have minimal effect on the allele frequencies (Garte et al. 2001).

In conclusion, we found that high-DII diets are associated with increased risk of CRC in a hospital-based case–control study in Spain, which was more pronounced for colon cancer than for rectal cancer. The positive association differed according to the genotype of rs2243250 in the promoter region of the inflammatory gene IL-4. Future studies are needed to evaluate the potential use of DII as a global measure of inflammatory potential of diet in relation to CRC risk in prospective studies and its relation to genetic susceptibility.

Abbreviations

CI: 

Confidence interval

CRC: 

Colorectal cancer

CRP: 

C-reactive protein

DII: 

Dietary inflammatory index

IL: 

Interleukin

OR: 

Odds ratio

PPARG: 

Peroxisome proliferator-activated receptor-γ

SNPs: 

Single nucleotide polymorphisms

Declarations

Acknowledgments

This study was supported by the Spanish Instituto de Salud Carlos III (RTICCC RD06/0020, CIBERESP CB07/02/2005, and Grants PS09-1037 and PI11-01439) and also from the Spanish Association Against Cancer (AECC) Scientific Foundation, the Catalan Government DURSI Grant 2014SGR647, and the European Commission Grants FP7-COOP-Health-2007-B HiPerDART. Dr. Hébert was supported by an Established Investigator Award in Cancer Prevention and Control from the Cancer Training Branch of the US National Cancer Institute (K05 CA136975).

Authors’ Affiliations

(1)
Biomarkers Group, Nutrition and Metabolism Section, International Agency for Research on Cancer (IARC)
(2)
South Carolina Statewide Cancer Prevention and Control Program, University of South Carolina
(3)
Department of Epidemiology and Biostatistics, Arnold School of Public Health, University of South Carolina
(4)
Genomic Epidemiology Group, German Cancer Research Center (DKFZ)
(5)
Department of Biology, University of Pisa
(6)
Cancer Prevention and Control Program, Catalan Institute of Oncology (ICO) and CIBERESP
(7)
Bellvitge Biomedical Research Institute (IDIBELL)
(8)
Department of Clinical Sciences, Faculty of Medicine, University of Barcelona

References

  1. Aleksandrova K, Jenab M, Boeing H, Jansen E, Bueno-de-Mesquita HB, Rinaldi S, Riboli E, Overvad K, Dahm CC, Olsen A, Tjønneland A, Boutron-Ruault MC, Clavel-Chapelon F, Morois S, Palli D, Krogh V, Tumino R, Vineis P, Panico S, Kaaks R, Rohrmann S, Trichopoulou A, Lagiou P, Trichopoulos D, van Duijnhoven FJ, Leufkens AM, Peeters PH, Rodríguez L, Bonet C, Sánchez MJ, Dorronsoro M, Navarro C, Barricarte A, Palmqvist R, Hallmans G, Khaw KT, Wareham N, Allen NE, Spencer E, Romaguera D, Norat T, Pischon T (2010) Circulating C-reactive protein concentrations and risks of colon and rectal cancer: a nested case–control study within the European Prospective Investigation into Cancer and Nutrition. Am J Epidemiol 172(4):407–418. doi:10.1093/aje/kwq135 PubMedView ArticleGoogle Scholar
  2. Candela M, Turroni S, Biagi E, Carbonero F, Rampelli S, Fiorentini C, Brigidi P (2014) Inflammation and colorectal cancer, when microbiota-host mutualism breaks. World J Gastroenterol 20(4):908–922. doi:10.3748/wjg.v20.i4.908 PubMed CentralPubMedView ArticleGoogle Scholar
  3. Cavicchia PP, Steck SE, Hurley TG, Hussey JR, Ma Y, Ockene IS, Hébert JR (2009) A new dietary inflammatory index predicts interval changes in serum high-sensitivity C-reactive protein. J Nutr 139(12):2365–2372. doi:10.3945/jn.109.114025 PubMed CentralPubMedView ArticleGoogle Scholar
  4. Coussens LM, Werb Z (2002) Inflammation and cancer. Nature 420(6917):860–867PubMed CentralPubMedView ArticleGoogle Scholar
  5. EPIC Group of Spain (1997a) Relative validity and reproducibility of a diet history questionnaire in Spain. II. Nutrients. EPIC Group of Spain. European prospective investigation into cancer and nutrition. Int J Epidemiol 26(Suppl 1):S100–S109Google Scholar
  6. EPIC Group of Spain (1997b) Relative validity and reproducibility of a diet history questionnaire in Spain. I. Foods. EPIC Group of Spain. European prospective investigation into cancer and nutrition. Int J Epidemiol 26(Suppl 1):S91–S99Google Scholar
  7. Ferlay J, Soerjomataram I, Ervik M, Dikshit R, Eser E, Mathers C, Rebelo M, Parkin DM, Forman D, Bray F (2013) GLOBOCAN 2012 v1.0, Cancer incidence and mortality worldwide: IARC CancerBase no. 11. Lyon, France: International Agency for Research on Cancer. http://globocan.iarc.fr. Accessed 30 Sept 2013
  8. Garte S, Gaspari L, Alexandrie AK, Ambrosone C, Autrup H, Autrup JL, Baranova H, Bathum L, Benhamou S, Boffetta P, Bouchardy C, Breskvar K, Brockmoller J, Cascorbi I, Clapper ML, Coutelle C, Daly A, Dell’Omo M, Dolzan V, Dresler CM, Fryer A, Haugen A, Hein DW, Hildesheim A, Hirvonen A, Hsieh LL, Ingelman-Sundberg M, Kalina I, Kang D, Kihara M, Kiyohara C, Kremers P, Lazarus P, Le Marchand L, Lechner MC, van Lieshout EM, London S, Manni JJ, Maugard CM, Morita S, Nazar-Stewart V, Noda K, Oda Y, Parl FF, Pastorelli R, Persson I, Peters WH, Rannug A, Rebbeck T, Risch D, Strange RC, Stücker I, Sugimura H, To-Figueras J, Vineis P, Yu MC, Taioli E (2001) Metabolic gene polynorphism frequencies in control populations. Cancer Epidemiol Biomark Prev 10(12):1239–1248Google Scholar
  9. Hebert JR, Shivappa N, Tabung FK, Steck SE, Wirth MD, Hurley TG (2014) On the use of the dietary inflammatory index in relation to low grade inflammation and markers of glucose metabolism in the Cohort study on Diabetes and Atherosclerosis Maastricht (CODAM) and the Hoorn study. Am J Clin Nutr 99(6):1520PubMedView ArticleGoogle Scholar
  10. Infante-Rivard C (2003) Hospital or population controls for case–control studies of severe childhood diseases? Am J Epidemiol 157(7):176–182PubMedView ArticleGoogle Scholar
  11. Landi S, Moreno V, Gioia-Patricola L, Guinó E, Navarro M, de Oca J, Capella G, Canzian F (2003) Association of common polymorphisms in inflammatory genes interleukin (IL)6, IL8, tumor necrosis factor alpha, NFKB1, and peroxisome proliferator-activated receptor gamma with colorectal cancer. Cancer Res 63(13):3560–3566PubMedGoogle Scholar
  12. Landi S, Bottari F, Gemignani F, Gioia-Patricola L, Guinó E, Osorio A, de Oca J, Capella G, Canzian F, Moreno V (2007) Interleukin-4 and interleukin-4 receptor polymorphisms and colorectal cancer risk. Eur J Cancer 43:762–768PubMedView ArticleGoogle Scholar
  13. Laukoetter MG, Mennigen R, Hannig CM, Osada N, Ricken E, Vowinkel T, Krieglstein CF, Senninger N, Anthoni C, Bruewer M (2011) Intestinal cancer risk in Crohn’s disease: a meta-analysis. J Gastrointest Surg 15(4):576–583. doi:10.1007/s11605-010-1402-9 PubMedView ArticleGoogle Scholar
  14. Liu S, Li T, Liu J (2012) Interleukin-4 rs2243250 polymorphism is associated with asthma among Caucasians and related to atopic asthma. Cytokine 59(2):364–369. doi:10.1016/j.cyto.2012.05.006 PubMedView ArticleGoogle Scholar
  15. McClellan JL, Davis JM, Steiner JL, Day SD, Steck SE, Carmichael MD, Murphy EA (2012) Intestinal inflammatory cytokine response in relation to tumorigenesis in the Apc(Min/+) mouse. Cytokine 57(1):113–119. doi:10.1016/j.cyto.2011.09.027 PubMed CentralPubMedView ArticleGoogle Scholar
  16. Murphy N, Norat T, Ferrari P, Jenab M, Bueno-de-Mesquita B, Skeie G, Dahm CC, Overvad K, Olsen A, Tjonneland A, Clavel-Chapelon F, Boutron-Ruault MC, Racine A, Kaaks R, Teucher B, Boeing H, Bergmann MM, Trichopoulou A, Trichopoulos D, Lagiou P, Palli D, Pala V, Panico S, Tumino R, Vineis P, Siersema P, van Duijnhoven F, Peeters PH, Hjartaker A, Engeset D, González CA, Sánchez MJ, Dorronsoro M, Navarro C, Aedanaz E, Quirós JR, Sonestedt E, Ericson U, Nilsson L, Palmqvist R, Khaw KT, Wareham N, Key TJ, Crowe FL, Fedirko V, Wark PA, Chuang SC, Riboli E (2012) Dietary fibre intake and risks of cancers of the colon and rectum in the European prospective investigation into cancer and nutrition (EPIC). PLoS ONE 7(6):e39361. doi:10.1371/journal.pone.0039361 PubMed CentralPubMedView ArticleGoogle Scholar
  17. Paffen E, Medina P, de Visser MC, van Wijngaarden A, Zorio E, Estellés A, Rosendaal FR, España F, Bertina RM, Doggen CJ (2008) The −589 C>T polymorphism in the interleukin-4 gene (IL-4) is associated with a reduced risk of myocardial infarction in young individuals. J Thromb Haemost 6(10):1633–1638. doi:10.1111/j.1538-7836.2008.03096.x PubMedView ArticleGoogle Scholar
  18. Shivappa N, Prizment AE, Blair CK, Jacobs DR Jr, Steck SE, Hébert JR (2014a) Dietary inflammatory index (DII) and risk of colorectal cancer in Women’s Health Study. Cancer Epidemiol Biomark Prev 23(11):2383–2392. doi:10.1158/1055-9965.EPI-14-0537 View ArticleGoogle Scholar
  19. Shivappa N, Steck SE, Hurley TG, Hussey JR, Hébert JR (2014b) Designing and developing a literature-derived, population-based dietary inflammatory index. Public Health Nutr 17(8):1689–1696. doi:10.1017/S1368980013002115 PubMedView ArticleGoogle Scholar
  20. Shivappa N, Steck SE, Hurley TG, Hussey JR, Ma Y, Ockene IS, Tabung F, Hébert JR (2014c) A population-based dietary inflammatory index predicts levels of C-reactive protein in the seasonal variation of blood cholesterol study (SEASONS). Public Health Nutr 17(8):1825–1833. doi:10.1017/S1368980013002565 PubMedView ArticleGoogle Scholar
  21. Slimani N, Deharveng G, Unwin I, Southgate DA, Vignat J, Skeie G, Salvini S, Parpinel M, Møller A, Ireland J, Becker W, Farran A, Westenbrink S, Vasilopoulou E, Unwin J, Borgejordet A, Rohrmann S, Church S, Gnagnarella P, Casagrande C, van Bakel M, Niravong M, Boutron-Ruault MC, Stripp C, Tjønneland A, Trichopoulou A, Georga K, Nilsson S, Mattisson I, Ray J, Boeing H, Ocké M, Peeters PH, Jakszyn P, Amiano P, Engeset D, Lund E, de Magistris MS, Sacerdote C, Welch A, Bingham S, Subar AF, Riboli E (2007) The EPIC nutrient database project (ENDB): a first attempt to standardize nutrient databases across the 10 European countries participating in the EPIC study. Eur J Clin Nutr 61(9):1037–1056PubMedView ArticleGoogle Scholar
  22. Sole X, Guino E, Valls J, Iniesta R, Moreno V (2006) SNPStats: a web tool for the analysis of association studies. Bioinformatics 22(15):1928–1929PubMedView ArticleGoogle Scholar
  23. Song M, Wu K, Ogino S, Fuchs CS, Giovannucci EL, Chan AT (2013) A prospective study of plasma inflammatory markers and risk of colorectal cancer in men. Br J Cancer 108(9):1891–1898. doi:10.1038/bjc.2013.172 PubMed CentralPubMedView ArticleGoogle Scholar
  24. van Duijnhoven FJ, Bueno-de-Mesquita HB, Ferrari P, Jenab M, Boshuizen HC, Ros MM, Casagrande C, Tjonneland A, Olsen A, Overvad K, Thorlacius-Ussing O, Clavel-Chapelon F, Boutron-Ruault MC, Morois S, Kaaks R, Linseisen J, Boeing H, Nöthlings U, Trichopoulou A, Trichopoulos D, Misirli G, Palli D, Sieri S, Panico S, Tumino R, Vineis P, Peeters PH, van Gils CH, Ocké MC, Lund E, Engeset D, Skeie G, Suárez LR, González CA, Sánchez MJ, Dorronsoro M, Navarro C, Barricarte A, Berglund G, Manjer J, Hallmans G, Palmqvist R, Bingham SA, Khaw KT, Key TJ, Allen NE, Boffetta P, Slimani N, Rinaldi S, Gallo V, Norat T, Riboli E (2009) Fruit, vegetables, and colorectal cancer risk: the European prospective investigation into cancer and nutrition. Am J Clin Nutr 89(5):1441–1452. doi:10.3945/ajcn.2008.27120 PubMedView ArticleGoogle Scholar
  25. van Woudenbergh GJ, Theofylaktopoulou D, Kuijsten A, Ferreira I, van Greevenbroek MM, van der Kallen CJ, Schalkwijk CG, Stehouwer CD, Ocke MC, Nijpels G, Dekker JM, Blaak EE, Feskens EJ (2013) Adapted dietary inflammatory index and its association with a summary score for low-grade inflammation and markers of glucose metabolism: the Cohort study on Diabetes and Atherosclerosis Maastricht (CODAM) and the Hoorn study. Am J Clin Nutr 98:1533–1542. doi:10.3945/ajcn.112.056333 PubMedView ArticleGoogle Scholar
  26. Wang D, DuBois RN (2013) The role of anti-inflammatory drugs in colorectal cancer. Annu Rev Med 64:131–144. doi:10.1146/annurev-med-112211-154330 PubMedView ArticleGoogle Scholar
  27. Wang LS, Kuo CT, Huang YW, Stoner GD, Lechner JF (2012) Gene–diet interactions on colorectal cancer risk. Curr Nutr Rep 1:132–141PubMed CentralPubMedView ArticleGoogle Scholar
  28. Wei EK, Giovannucci E, Wu K, Rosner B, Fuchs CS, Willett WC, Colditz GA (2004) Comparison of risk factors for colon and rectal cancer. Int J Cancer 108(3):433–442PubMed CentralPubMedView ArticleGoogle Scholar
  29. Wood LG, Shivappa N, Berhton BS, Gibson PG, Hébert JR (2014) Dietary inflammatory index is related to asthma risk, lung function and systemic inflammation in asthma. Clin Exp Allergy. doi:10.1111/cea.12323 Google Scholar
  30. World Research Cancer Fund, American Institute for Cancer Research (2007) Food, nutrition, physical activity and the prevention of cancer: a global perspective. Washington, DCGoogle Scholar
  31. Wu J, Cai Q, Li H, Cai H, Gao J, Yang G, Zheng W, Xiang YB, Shu XO (2013) Circulating C-reactive protein and colorectal cancer risk: a report from the Shanghai Men’s Health Study. Carcinogenesis 34(12):2799–2803. doi:10.1093/carcin/bgt288 PubMed CentralPubMedView ArticleGoogle Scholar
  32. Zamora-Ros R, Knaze V, Luján-Barroso L, Romieu I, Scalbert A, Slimani N, Hjartåker A, Engeset D, Skeie G, Overvad K, Bredsdorff L, Tjønneland A, Halkjær J, Key TJ, Khaw KT, Mulligan AA, Winkvist A, Johansson I, Bueno-de-Mesquita HB, Peeters PH, Wallström P, Ericson U, Pala V, de Magistris MS, Polidoro S, Tumino R, Trichopoulou A, Dilis V, Katsoulis M, Huerta JM, Martínez V, Sánchez MJ, Ardanaz E, Amiano P, Teucher B, Grote V, Bendinelli B, Boeing H, Förster J, Touillaud M, Perquier F, Fagherazzi G, Gallo V, Riboli E, González CA (2013a) Differences in dietary intakes, food sources, and determinants of total flavonoids between Mediterranean and non-Mediterranean countries participating in the European prospective investigation into cancer and nutrition (EPIC) study. Br J Nutr 109(8):1498–1507. doi:10.1017/S0007114512003273 PubMedView ArticleGoogle Scholar
  33. Zamora-Ros R, Not C, Guinó E, Luján-Barroso L, García RM, Biondo S, Salazar R, Moreno V (2013b) Association between habitual dietary flavonoid and lignan intake and colorectal cancer in a Spanish case–control study (the Bellvitge Colorectal Cancer Study). Cancer Causes Control 24(3):549–557. doi:10.1007/s10552-012-9992-z PubMedView ArticleGoogle Scholar
  34. Zheng Z, Li X, Li Z, Ma XC (2013) IL-4 −590C/T polymorphism and susceptibility to liver disease: a meta-analysis and meta-regression. DNA Cell Biol 32(8):443–450. doi:10.1089/dna.2013.2020 PubMedView ArticleGoogle Scholar
  35. Zhenzhen L, Xianghua L, Qingwei W, Zhan G, Ning S (2013) Three common polymorphisms in the IL-4 gene and cancer risk: a meta-analysis involving 5,392 cases and 6,930 controls. Tumour Biol 34(4):2215–2224. doi:10.1007/s13277-013-0761-8 PubMedView ArticleGoogle Scholar

Copyright

© Springer-Verlag Berlin Heidelberg 2014

Advertisement