Abstract
Purpose
Inflammation is a process central to carcinogenesis, and in particular to colorectal cancer (CRC). Previously, we developed a dietary inflammatory index (DII) from extensive literature review to assess the inflammatory potential of diet. In the current study, we utilized this novel index in the Women’s Health Initiative (WHI) to prospectively evaluate its association with risk of CRC in postmenopausal women.
Methods
The DII was calculated from baseline food frequency questionnaires administered to 152,536 women aged 50–79 years without CRC at baseline between 1993 and 1998 and followed through September 30, 2010. Incident CRC cases were ascertained through a central physician adjudication process. Multiple covariate-adjusted Cox proportional hazards regression models were used to estimate hazard ratios (HR) and 95% confidence intervals (95%CI) for colorectal, colon (proximal/distal locations), and rectal cancer risk, by DII quintiles(Q).
Results
During an average 11.3 years of follow-up, a total of 1,920 cases of colorectal cancer (1,559 colon and 361 rectal) were identified. Higher DII scores (representing a more pro-inflammatory diet) were associated with an increased incidence of colorectal cancer (HRQ5-Q1, 1.22; 95% CI, 1.05, 1.43; Ptrend=0.02) and colon cancer, specifically proximal colon cancer (HRQ5-Q1, 1.35; 95% CI, 1.05, 1.67; Ptrend=0.01) but not distal colon cancer (HRQ5-Q1, 0.84; 95% CI, 0.61, 1.18; Ptrend=0.63) or rectal cancer (HRQ5-Q1, 1.20; 95% CI, 0.84, 1.72; Ptrend=0.65).
Conclusion
Consumption of pro-inflammatory diets is associated with an increased risk of CRC, especially cancers located in the proximal colon. The absence of a significant association for distal colon cancer and rectal cancer may be due to the small number of incident cases for these sites. Interventions that may reduce the inflammatory potential of the diet are warranted to test our findings, thus provide more information for colon cancer prevention.
Keywords: dietary inflammatory index, colorectal cancer, Women’s Health Initiative
INTRODUCTION
Inflammation is a process central to carcinogenesis and other chronic diseases, and there is evidence that diet modulates in ammation (1–4). Chronic in ammatory conditions are associated with cancer risk, for example, patients with inflammatory bowel disease have an increased risk of developing colorectal cancer (5, 6). Moreover, several studies have shown a reduced risk of colon cancer with use of aspirin or other anti-inflammatory agents (7–9).
Specific components of the diet have been shown to be associated with lower levels of inflammation; e.g., fruits and vegetables, omega-3 polyunsaturated fatty acids (PUFAs), fiber, moderate alcohol intake (10, 11). Such components of diet are generally known to have a much wider safety margin with prudent use than do pharmaceutical agents (12). In contrast, dietary components such as saturated fatty acids (SFA), high-glycemic index foods, and a high ω-6/ω-3 PUFA ratio are associated with increased levels of inflammation (4, 13–15). Given that nutrients or foods are not consumed in isolation, a protective or deleterious effect of diet will likely include a combination of these dietary factors (16). Dietary pattern analysis can provide an approach to examining the relationship between diet and the risk of chronic diseases that produces more intuitively appealing results that may be more predictive of disease risk than are individual foods or nutrients (3, 10, 17–19).
The dietary inflammatory index (DII) was developed (20) and construct validated (21) to assess the overall quality of diet with regard to its inflammatory potential. We previously found that food frequency questionnaire (FFQ)-derived DII scores were significantly associated with inflammatory biomarkers, where higher DII scores (representing more pro-inflammatory diets) were positively associated with interleukin-6 (IL-6), tumor necrosis factor alpha receptor 2 (TNFα-R2) and high sensitivity C-reactive protein (hs-CRP) in a subset of women in the Women’s Health Initiative (WHI) (unpublished results: Tabung, Steck, Zhang, Ma, Liese, Agalliu, et al., 2014). In addition, we observed that a higher DII score was associated with increased risk of colorectal cancer in the Iowa Women’s Health Study, with a hazard ratio (HR) of 1.20 [95% confidence interval (CI), 1.01–1.43] comparing the highest with the lowest DII quintile (22) as well as in the Bellvitge Colorectal Cancer Case-control Study where the odds ratio for the fourth quartile compared to the first was 1.65 (95%CI, 1.05–2.60) (23). Our objective in the current study was to examine whether pro-inflammatory diets, as measured by the DII, are associated with increased risk of colorectal cancer in the WHI, a larger, more racially and geographically diverse population of postmenopausal women in the United States.
METHODS
Participants
The WHI is a large clinical investigation of strategies for the prevention and control of some of the most common causes of morbidity and mortality among postmenopausal women. The design of the WHI has been described in detail elsewhere (24). Briefly, the WHI enrolled a total of 161,808 postmenopausal women 50 to 79 years old, in 40 clinical centers across the United States between 1993 and 1998. The women were enrolled into either the Clinical Trials (CT) that included 68,132 women or the Observational Study (OS) that included 93,676 women. There were three overlapping components of the CT, including the Dietary Modification Trial (DMT), Hormone Therapy Trials (HT; which included an estrogen-plus-progestin study of women with a uterus and the estrogen-alone study of women without a uterus) and the Calcium and Vitamin D Trial (CaD). Women who proved to be ineligible for, or who were unwilling to enroll in, the CT were invited to be part of the prospective cohort of women in the OS (24). Women of racial/ethnic minority groups represented 17.1% of the overall sample.
Exclusion criteria for both the OS and CT included any medical condition associated with a predicted survival of less than three years, alcoholism, other drug dependency, mental illness (e.g., major depressive disorder), dementia, active participation in another intervention trial and not likely to live in the area for at least three years. Demographic information and dietary data were obtained by self-report using standardized questionnaires. Certified staff drew blood samples from participants and performed physical exam and measurements, including blood pressure, height and weight, at the baseline clinic visit. Women were further excluded from the DM if their FFQ-assessed diets had <32% energy from fat (25). The WHI protocol was approved by the institutional review boards at the Clinical Coordinating Center (CCC) at the Fred Hutchinson Cancer Research Center (Seattle, WA) and at each of the 40 Clinical Centers (26).
Diet assessment
As part of baseline enrollment screening for the WHI, all participants completed a standardized 122-item FFQ developed for the WHI to estimate average daily nutrient intake over the previous three-month period (25). FFQs were mailed to participants who completed and returned them to the Clinical Coordinating Centers. FFQ data were considered complete if all adjustment questions, all summary questions, 90% of the foods, and at least one-half of every food group section were complete (27). The nutrient database, linked to the University of Minnesota Nutrition Data System for Research (NDSR) (28), is based on the US Department of Agriculture Standard Reference Releases and manufacturer information. This FFQ has demonstrated good comparability to 24-hour dietary recall interviews and food records in the WHI (25). When compared to biomarkers such as doubly labeled water and urinary nitrogen, the FFQ was shown to underestimate energy intake by 27–32% and protein intake by 10–15% (29, 30).
Description of the dietary inflammatory index (DII)
Details of the development (20) and construct validation (21) of the DII have been previously described. Briefly, an extensive literature search was performed to obtain peer-reviewed journal articles that examined the association between six well known inflammatory biomarkers (IL-1β, IL-4, IL-6, IL-10, TNFα, and CRP) and 45 specific foods and nutrients (components of the DII). Literature-derived inflammatory effect scores for each of the DII components were standardized to a representative global diet database, constructed based on 11 datasets from diverse populations in different parts of the world. Overall DII scores for each individual participant represent the sum of each of the DII components in relation to the comparison global diet database (20). The DII score characterizes an individual’s diet on a continuum from maximally anti-inflammatory to maximally pro-inflammatory, with a higher DII score indicating a more pro-inflammatory diet and a lower (more negative) DII score indicating a more anti-inflammatory diet. In the WHI FFQ, 32 of the 45 original DII components were available for inclusion in the overall DII score (see (20) for list of 45 DII components). Components such as ginger, turmeric, garlic, oregano, hot pepper, rosemary, eugenol, saffron, flavan-3-ol, flavones, flavonols, flavonones, anthocyanidins that are included in the original DII calculation (20) were not included in the current study because they were not available from the WHI FFQ. The absence of these components is likely to have a minimal impact on overall DII scores because most of the missing food items are consumed in small quantities in this population.
Outcomes ascertainment
The WHI outcomes ascertainment and adjudication methods have been previously described (31). Briefly, participants (or next-of-kin) self-reported cancer diagnoses on questionnaires annually in the OS or semiannually in the CT through 2005 and annually in both the OS and CT, thereafter. CRC events reported were verified by centrally trained physician adjudicators after review of medical records and pathology reports.
The outcome for these analyses was colorectal cancer, including cancers of the colon and rectum (including rectum and rectosigmoid). Proximal colon cancers were defined as cancers of the cecum, ascending colon, right colon, hepatic flexure of colon, and transverse colon (ICD=C18.0, C18.2–18.4), and distal colon cancers were defined as cancers of the splenic flexure of colon, descending colon, left colon and sigmoid colon (ICD=C18.5–18.7). Separate analyses also were conducted considering stage of colorectal cancer at diagnosis (localized, regional and distant).
Covariates
Covariates included in the models were as follows: total energy intake (kcal/day); age (years); body mass index [BMI= weight(kg)/height(m)2] categorized into normal weight (<25kg/m2), overweight (25–<30 kg/m2), and obese (≥30kg/m2); race groups, European American (EA), African American (AA), Hispanic (HP), and Asian or Pacific Islander (A/PI); educational levels were categorized into less than high school, some high school /GED, at least some college/graduate education; smoking status was categorized into current, past, and never; physical activity (PA) was categorized based on current public health recommendations (32), as meeting or not meeting PA recommendations (≥150 minutes/week of moderate intensity PA or ≥75 minutes/week of vigorous intensity PA versus<150 minutes/week of moderate intensity PA or <75 minutes/week of vigorous intensity PA, respectively); family history of colorectal cancer (yes/no); diabetes (yes/no); hypertension (yes/no); arthritis (yes/no); history of colonoscopy/sigmoidoscopy (yes/no); history of occult blood tests (yes/no); non-steroidal anti-inflammatory drug (NSAID) use (yes/no); category and duration of estrogen use and category and duration of combined estrogen and progesterone use both categorized into five groups (none, <5y, 5 to <10y, 10 to <15y, and ≥15y); DMT arm (intervention, control, not randomized to DMT); HT arm (estrogen-alone intervention, estrogen-alone control, combined estrogen and progesterone intervention, estrogen and progesterone control, not randomized to HT); and CaD arm (intervention, control, not randomized to CaD). Data on potential confounders were collected by self-administered questionnaires on demographics, medical history, and lifestyle factors (24).
Statistical analyses
Data from both components (OS and CT) of the WHI were utilized. Women who reported previous colorectal cancer at baseline or who were missing previous colorectal cancer status at baseline were excluded (n=2,387), as well as women with implausible reported total energy intake values (≤ 600 kcal/d or ≥ 5000 kcal/d, n=4,688) or extreme BMI values (≤ 15kg/m2 or ≥ 50kg/m2, n=2,142). Frequencies and percentages were computed to describe the distribution of covariates across quintiles of the DII.
Cox proportional hazards (PH) regression models were used to calculate HRs, 95% 95% CIs and linear trends for risk of CRC, colon and rectal cancers, by DII quintiles and with adjustment for multiple covariates. Models also were constructed separately for proximal colon cancer and distal colon cancer, as well as for CRC stage at diagnosis. The PH assumption was assessed for each covariate using Martingale-based residuals. Smoking status and CaD arm violated the PH assumption; therefore all Cox models were stratified by these two covariates. The lowest DII quintile (representing the most anti-inflammatory diet) was the referent for all models. Potential effect modification of the association between the DII and colorectal cancer by age group, educational level, smoking status, NSAID use, waist-to-hip ratio, waist circumference, race/ethnicity, and BMI, was investigated by stratifying the Cox PH models by levels of the potential effect modifier. Significant effect modification was considered at a P-value of 0.10 for the DII*covariate interaction term. Potential confounders that changed HRs by >10% were retained in the final model. Tests of linear trend between colorectal cancer incidence and increments of DII score adjusted for covariates were computed by assigning the median value of each quintile to each participant in the quintile, and this variable was entered into models as ordinal values. In sensitivity analyses, colorectal cancer cases that occurred within three years from baseline were excluded to reduce the likelihood that baseline diet may have changed recently due to presence of subclinical disease. Statistical analyses were conducted using SAS version 9.3® (SAS Institute, Cary, NC). All tests were two-sided.
RESULTS
Table 1 presents the distribution of participants’ characteristics across quintiles of the DII. Participants with higher DII scores (representing a more pro-inflammatory diet) consisted of a higher proportion of women who were overweight or obese, not meeting PA guidelines, with lower educational attainment, and current smokers.
Table 1.
Participant characteristics (n, %) by quintiles of the dietary inflammatory index (DII) at baseline; Women’s Health Initiative, 1993–1998
| Characteristic | Q1 (−7.055, <− 3.136) (healthiest) n=30508 | Q2 (−3.136, <− 1.995) n=30507 | Q3 (−1.995, <− 0.300) n=30507 | Q4 (−0.300, 1.953) n=30506 | Q5 (1.953, 5.636) (least healthy) n=30508 |
|---|---|---|---|---|---|
| Age categories, year | |||||
| 50–59 | 9242 (30.3) | 9276 (30.4) | 9960 (32.6) | 10628 (34.8) | 11584 (38.0) |
| 60–69 | 14147 (46.4) | 14024 (46.0) | 13672 (44.8) | 13607 (44.6) | 13194 (43.2) |
| 70+ | 7119 (23.3) | 7207 (23.6) | 6875 (22.6) | 6271 (20.6) | 5730 (18.8) |
| Body mass index/ kg/m2 | |||||
| Normal (<25) | 12973 (42.5) | 11378 (37.3) | 10635 (34.9) | 9795 (32.2) | 9095 (29.8) |
| Overweight (25 – <30) | 10368 (34.0) | 10638 (34.9) | 10710 (35.1) | 10780 (35.3) | 10752 (35.2) |
| Obesity (≥ 30) | 7167 (23.5) | 8491 (27.8) | 9162 (30.0) | 9931 (32.5) | 10661 (35.0) |
| Race/ethnicity | |||||
| American Indian or Alaskan Native | 87 (0.3) | 89 (0.3) | 124 (0.4) | 141 (0.5) | 189 (0.6) |
| Asian or Pacific Islander | 1332 (4.4) | 625 (2.1) | 658 (2.2) | 783 (2.6) | 522 (1.7) |
| African-American | 1358 (4.5) | 1783 (5.8) | 2313 (7.6) | 2867 (9.4) | 4644 (15.2) |
| Hispanic/Latino | 638 (2.1) | 785 (2.6) | 1140 (3.7) | 1319 (4.3) | 1918 (6.3) |
| European American | 26661 (87.4) | 26840 (88.0) | 25860 (84.8) | 24978 (82.0) | 22792 (74.7) |
| Other | 366 (1.1) | 320 (1.0) | 333 (1.1) | 345 (1.1) | 359 (1.2) |
| Missing | 66 (0.2) | 65 (0.2) | 79 (0.2) | 73 (0.2) | 84 (0.3) |
| Physical activity (PA) | |||||
| Not meeting PA recommendations | 13049 (42.8) | 16102 (52.8) | 17326 (56.8) | 17999 (59.0) | 20324 (66.6) |
| Meeting PA recommendations | 16601 (54.4) | 13321 (43.7) | 11817 (38.7) | 10819 (35.5) | 8378 (27.5) |
| Missing | 856 (2.8) | 1081 (3.5) | 1367 (4.5) | 1693 (5.5) | 1805 (5.9) |
| Educational level | |||||
| <High school | 738 (2.4) | 1141 (3.7) | 1472 (4.8) | 1723 (5.6) | 2579 (8.4) |
| Some high school/GED | 13922 (45.6) | 16358 (53.6) | 16968 (55.6) | 17166 (56.4) | 18755 (61.5) |
| At least some years of college | 15642 (51.3) | 12784 (42.0) | 11875 (39.0) | 11385 (37.2) | 8916 (29.2) |
| Missing | 206 (0.7) | 224 (0.7) | 192 (0.6) | 232 (0.8) | 258 (0.9) |
| Smoking status | |||||
| Never | 15128 (49.6) | 15415 (50.5) | 15618 (51.2) | 15414 (50.5) | 15234 (49.9) |
| Past | 13911 (45.6) | 13094 (43.0) | 12462 (40.8) | 12489 (40.9) | 11571 (37.9) |
| Current | 1101 (3.6) | 1680 (5.5) | 2068 (6.8) | 2224 (7.3) | 3323 (10.9) |
| Missing | 368 (1.2) | 318 (1.0) | 359 (1.2) | 379 (1.2) | 380 (1.3) |
| Family history of colorectal cancer | |||||
| No | 26590 (87.2) | 26418 (86.6) | 26525 (86.9) | 26471 (86.8) | 26444 (86.7) |
| Yes | 2369 (7.8) | 2525 (8.3) | 2412 (7.9) | 2389 (7.8) | 2286 (7.5) |
| Missing | 1549 (5.0) | 1564 (5.1) | 1570 (5.2) | 1646 (5.4) | 1778 (5.8) |
| NSAID use | |||||
| No | 12988 (42.6) | 12302 (40.3) | 13090 (42.9) | 13803 (45.3) | 14597 (47.8) |
| Yes | 17520 (57.4) | 18205 (59.7) | 17417 (57.1) | 16703 (54.7) | 15911 (52.2) |
During an average 11.3 years of follow-up, a total of 1,920 cases of colorectal cancer (1,559 colon and 361 rectal) were identified. In the main analysis, consumption of more pro-inflammatory diets was associated with an increased risk of colorectal cancer, comparing the highest with the lowest DII quintile (HR, 1.22; 95% CI, 1.05, 1.43; Ptrend=0.02) and colon cancer (HR, 1.23; 95%CI, 1.03, 1.46; Ptrend=0.02). The hazard ratio for rectal cancer also was elevated in the fifth quintile but was not statistically significant (HR, 1.20; 95%CI, 0.84, 1.72; Ptrend=0.65). There was statistically significantly higher risk of proximal colon cancer, but not distal colon cancer (Table 2), for women in the highest DII quintile. The magnitude of risk estimates increased when colorectal cancer cases that developed within three years from baseline were excluded. For example, the hazard ratios were 1.30 (95%CI, 1.09, 1.56; Ptrend=0.008) for colorectal cancer and 1.36 (95%CI, 1.11, 1.66; Ptrend=0.003) for colon cancer comparing the highest with the lowest DII quintile (Table 3).
Table 2.
Hazard ratios and 95% confidence intervals for colorectal cancer risk across quintiles of the dietary inflammatory index; Women’s Health Initiative, 1993–2010
| Q1 (−7.055, <− 3.136) (healthiest) | Q2 (−3.136, <− 1.995) | Q3 (−1.995, <− 0.300) | Q4 (−0.300, <1.953) | Q5 (1.953, 5.636) (least healthy) | ||
|---|---|---|---|---|---|---|
|
| ||||||
| Referent | HR (95%CI)a | HR (95%CI) | HR (95%CI) | HR (95%CI) | Ptrend | |
| Colorectal cancer | ||||||
| Age-adjusted model | 1.00 | 1.10 (0.96, 1.26) | 1.06 (0.93, 1.22) | 1.11 (0.97, 1.27) | 1.38 (1.21, 1.57)* | <0.0001 |
| Multivariable-adjusted modelb | 1.00 | 1.05 (0.91, 1.21) | 0.98 (0.84, 1.13) | 1.02 (0.88, 1.19) | 1.22 (1.05, 1.43)* | 0.02 |
| Colorectal cancer cases, 1920 | 365 (19.0%) | 388 (20.2%) | 359 (18.7%) | 373 (19.4%) | 435 (22.7%) | |
| Colon cancer | ||||||
| Age-adjusted model | 1.00 | 1.08 (0.93, 1.26) | 1.04 (0.89, 1.21) | 1.10 (0.95, 1.28) | 1.35 (1.17, 1.56)* | <0.0001 |
| Multivariable-adjusted model | 1.00 | 1.05 (0.89, 1.23) | 0.98 (0.83, 1.15) | 1.07 (0.91, 1.26) | 1.23 (1.03, 1.46)* | 0.02 |
| Colon cancer cases, 1559 | 299 (19.2%) | 314 (20.1%) | 288 (18.5%) | 312 (20.0%) | 346 (22.2%) | |
| Proximal colon (C18.0–18.4)c | ||||||
| Age-adjusted model | 1.00 | 1.18 (0.98, 1.41) | 1.00 (0.82, 1.21) | 1.15 (0.96, 1.38) | 1.35 (1.13, 1.62)* | 0.002 |
| Multivariable-adjusted model | 1.00 | 1.16 (0.96, 1.41) | 0.98 (0.79, 1.20) | 1.15 (0.94, 1.41) | 1.35 (1.09, 1.67)* | 0.01 |
| Proximal colon cancer cases, 1034d | 193 (18.7%) | 221 (21.4%) | 181 (17.5%) | 210 (20.3%) | 229 (22.2%) | |
| Distal colon (C18.5–18.7)c | ||||||
| Age-adjusted model | 1.00 | 0.90 (0.68, 1.20) | 1.06 (0.80, 1.39) | 1.02 (0.77, 1.35) | 1.21 (0.93, 1.59) | 0.08 |
| Multivariable-adjusted model | 1.00 | 0.80 (0.58, 1.09) | 0.91 (0.67, 1.23) | 0.90 (0.67, 1.22) | 0.84 (0.61, 1.18) | 0.63 |
| Distal colon cancer cases, 428d | 90 (21.0%) | 76 (17.7%) | 88 (20.6%) | 88 (20.6%) | 86 (20.1%) | |
| Rectal cancere | ||||||
| Age-adjusted model | 1.00 | 1.19 (0.87, 1.63) | 1.17 (0.86, 1.61) | 1.15 (0.84, 1.58) | 1.48 (1.10, 2.01)* | 0.02 |
| Multivariable-adjusted model | 1.00 | 1.07 (0.76, 1.50) | 0.98 (0.70, 1.39) | 0.84 (0.58, 1.20) | 1.20 (0.84, 1.72) | 0.65 |
| Rectal cancer cases, 361 | 66 (18.3%) | 74 (20.5%) | 71 (19.7%) | 61 (16.9%) | 89 (24.6%) | |
Statistically significant;
Hazard ratio and 95% confidence interval;
All multivariable models were adjusted for age, total energy intake, body mass index, race/ethnicity, physical activity, educational level, smoking status, family history of colorectal cancer, hypertension, diabetes, arthritis, history of colonoscopy, history of occult blood tests, NSAID use, category and duration of estrogen use, category and duration of estrogen & progesterone use, dietary modification trial arm, hormone therapy trial arm and calcium and vitamin DMT trial arm;
ICD-O-2 codes used to define location of colon cancer include C18.0 (cecum), C18.2 (ascending colon, right colon), C18.3 (hepatic flexure of colon), C18.4 (transverse colon), C18.5 (splenic flexure of colon), C18.6 (descending colon, left colon) and C18.7 (sigmoid colon);
Proximal and distal colon cancer cases do not add up to the total number of colon cancer cases because of missing ICD codes and exclusion of codes C18.8 and C18.9 for large intestine Not Otherwise Specified;
Rectal cancer include all rectum and rectosigmoid cases.
Table 3.
Hazard ratios and 95% confidence intervals for colorectal cancer risk across quintiles of the dietary inflammatory index, excluding colorectal cancer cases which developed within three years from baseline; Women’s Health Initiative, 1993–2010
| Q1 (−7.055, <− 3.139) (healthiest) | Q2 (−3.139, <− 1.997) | Q3 (−1.997, <− 0.306) | Q4 (−0.306, <1.949) | Q5 (1.949, 5.636) (least healthy) | ||
|---|---|---|---|---|---|---|
|
| ||||||
| Referent | a HR (95%b CI) | HR (95%CI) | HR (95%CI) | HR (95%CI) | Ptrend | |
| Colorectal cancer | ||||||
| Age-adjusted modeld | 1.00 | 1.10 (0.94, 1.28) | 1.07 (0.92, 1.25) | 1.10 (0.94, 1.29) | 1.42 (1.23, 1.65)* | <0.0001 |
| Multivariable-adjusted modele | 1.00 | 1.07 (0.91, 1.27) | 1.00 (0.84, 1.19) | 1.04 (0.87, 1.23) | 1.30 (1.09, 1.56)* | 0.008 |
| Colorectal cancer cases, 1462 | 274 (18.7%) | 296 (20.2%) | 273 (18.7%) | 282 (19.3%) | 337 (23.1%) | |
| Colon cancer | ||||||
| Age-adjusted modeld | 1.00 | 1.12 (0.94, 1.33) | 1.05 (0.89, 1.25) | 1.13 (0.95, 1.34) | 1.46 (1.24, 1.72)* | <0.0001 |
| Multivariable-adjusted modele | 1.00 | 1.11 (0.92, 1.33) | 1.00 (0.82, 1.21) | 1.11 (0.92, 1.33) | 1.36 (1.11, 1.66)* | 0.003 |
| Colon cancer cases, 1202 | 222 (18.5%) | 246 (20.5%) | 218 (18.1%) | 239 (19.9%) | 277 (23.0%) | |
| Proximal colon (C18.0–18.4)c | ||||||
| Age-adjusted model | 1.00 | 1.20 (0.96, 1.48) | 0.96 (0.76, 1.21) | 1.20 (0.97, 1.50) | 1.45 (1.17, 1.80)* | 0.0007 |
| Multivariable-adjusted model | 1.00 | 1.18 (0.95, 1.46) | 0.93 (0.74, 1.18) | 1.15 (0.91, 1.44) | 1.38 (1.09, 1.75)* | 0.01 |
| Proximal colon cancer cases, 827d | 153 (18.5%) | 179 (21.6%) | 139 (16.8%) | 168 (20.3%) | 188 (22.7%) | |
| Distal colon (C18.5–18.7)c | ||||||
| Age-adjusted model | 1.00 | 0.91 (0.63, 1.33) | 1.17 (0.82, 1.67) | 1.01 (0.76, 1.58) | 1.23 (0.86, 1.75) | 0.17 |
| Multivariable-adjusted model | 1.00 | 0.86 (0.59, 1.26) | 1.08 (0.75, 1.55) | 0.97 (0.67, 1.41) | 1.03 (0.69, 1.54) | 0.71 |
| Distal colon cancer cases, 296d | 58 (19.6%) | 52 (17.6%) | 65 (22.0%) | 59 (19.9%) | 62 (20.9%) | |
| Rectal cancere | ||||||
| Age-adjusted modeld | 1.00 | 1.00 (0.69, 1.43) | 1.15 (0.81, 1.64) | 1.00 (0.69, 1.44) | 1.27 (0.89, 1.80)* | 0.22 |
| Multivariable-adjusted modele | 1.00 | 0.93 (0.63, 1.38) | 0.99 (0.67, 1.47) | 0.77 (0.51, 1.16) | 1.08 (0.71, 1.65) | 0.96 |
| Rectal cancer cases, 260 | 52 (20.0%) | 50 (19.2%) | 55 (21.5%) | 43 (16.5%) | 60 23.1%) | |
Statistically significant;
hazard ratio and associated 95% confidence interval;
all multivariable models were adjusted for total energy intake, age, body mass index, race/ethnicity, physical activity, educational level, smoking status, family history of colorectal cancer, hypertension, diabetes, arthritis, history of colonoscopy, history of occult blood tests, NSAID use, category and duration of estrogen use, category and duration of estrogen & progesterone use, dietary modification trial arm, hormone therapy trial arm and calcium and vitamin D trial arm;
ICD-O-2 codes used to define location of colon cancer include C18.0 for cecum, C18.2, ascending colon, right colon, C18.3, hepatic flexure of colon, C18.4, transverse colon, C18.5, splenic flexure of colon, C18.6, descending colon, left colon and C18.7, sigmoid colon;
proximal and distal colon cancer cases do not add up to the total number of colon cancer cases because of missing ICD codes and codes C18.8 and C18.9 for large intestine Not Otherwise Specified, were not included;
rectal cancer include all rectum and rectosigmoid cases.
The DII did not appear to be differentially associated with disease stage. Analysis in strata of potential effect modifiers showed differences in the association between DII and colorectal cancer in categories of NSAID use, where non-users of NSAIDs were at increased risk for colorectal cancer (HRQ5vsQ1, 1.31, 95%CI, 1.05, 1.65), while risk was not increased among regular users of NSAIDs (HRQ5vsQ1, 1.11, 95%CI, 0.89, 1.38). No other variables (age group, education, smoking, BMI, waist circumference, waist-to-hip ratio, or physical activity) were found to modify the association between the DII and colorectal cancer (data not shown).
DISCUSSION
In this large prospective examination of the association between the DII and colorectal cancer risk, more extreme pro-inflammatory diets (i.e., the highest quintile of intake) were associated with increased risk of colorectal cancer. The effects were most apparent for cancers located in the proximal colon. We found no substantial association between the DII and distal colon cancer or rectal cancer, though analyses were likely limited by the small number of incident cancer cases at these sites
Our findings are similar to previous results obtained from the Iowa Women’s Health Study, in which the highest quintile of DII was associated with 20% increased risk of colorectal cancer among postmenopausal women (22). Our results also support studies of overall diet quality and colorectal cancer risk (33–36). This is expected given that compliance with diets based on dietary recommendations are consistent with healthful culinary traditions [e.g., macrobiotic, dietary approaches to stop hypertension (DASH), and Mediterranean meal plans] that tend to be anti-inflammatory (37). For example, Miller et al. examined the association of four indices developed to capture the DASH dietary pattern and risk of colorectal cancer (33). Increased compliance to the DASH diet was consistently associated with reduced risk of colorectal cancer across all four DASH indices, though the extent of the predicted reduced risk depended on the method used to develop the DASH index. Another study using the DASH diet index also observed a reduced risk of colorectal cancer with higher index scores, and similar to the current study results, found a significantly reduced risk for colon but not rectal cancer (34). In another study, Reedy et al. examined the association of four different dietary indices and colorectal cancer risk, and found that higher scores on all four indices were associated with a decreased colorectal cancer risk in men, but only the Healthy Eating Index 2005 was associated with decreased risk in women (35).
In contrast to our study findings, the WHI DMT’s low-fat dietary pattern did not reduce risk of colorectal cancer in postmenopausal women after 8.1 years of follow-up (38). The targeted dietary intervention would have been expected to have anti-inflammatory effects by reducing dietary fat and increasing consumption of vegetables, fruits, and whole grains (38). The absence of an intervention effect on colorectal cancer risk could be due to an insufficiently large difference in dietary intake between the intervention and comparison groups and shorter follow-up duration compared to current investigation (38). Alternatively, the fact that the annualized incidence rates of colon polyps or adenomas (self-report) were lower in the intervention group than in the comparison group (2.16% vs 2.35%, respectively; HR, 0.91; 95% CI, 0.87–0.95) suggests that the intervention may have slowed progression to CRC or that more than 8.1 years of follow up was needed to detect clinically apparent CRC (38). In any event, these findings are mitigated somewhat in the current analyses by comparing dietary intake across all participants, regardless of intervention status, thus allowing for comparisons of participants across a broader range of intake. As with any observational study, however, the role of unmeasured or residual confounding cannot be entirely ruled out.
The difference in results by anatomic site, with significant associations in the proximal colon but not in the distal colon or rectum is similar to other studies, and supports the idea that CRC is a heterogeneous group of diseases. Studies have found differences in risk factors for colon and rectal cancer; for example, obesity - a state of low-grade chronic systemic inflammation - is associated with increased risk of colon cancer (39), but not rectal cancer (40). Biologically, rectal and distal colon tumors have been found to share similar mutational frequencies and other characteristics which are different from those observed in tumors of the proximal colon (41, 42) and may thus be influenced by different mechanisms of carcinogenesis (43).
Our results appeared to be modified by regular use of NSAIDs, where pro-inflammatory diets were associated with higher risk of CRC only among non-users of NSAIDs. The adverse effects of a pro-inflammatory diet on inflammation may be potentially masked by the stronger effects of NSAIDs among regular users. The anti-inflammatory effect of NSAIDs on the colonic epithelium could be so strong as to render inconsequential the relative contribution of dietary inflammatory potential, and thus a DII-CRC association would not be observed among NSAID users, yet would be strong among nonusers of NSAIDs(44–46).
Evidence from experimental models of colon carcinogenesis indicates that cytokines derived from inflammatory cells may drive the uncontrolled proliferation of cancer cells, either directly or indirectly (9, 47, 48). The link between inflammation and colon cancer is further supported by evidence from studies showing a positive association between higher concentrations of inflammatory biomarkers and increased risk of colon cancer (49–51); or a reduced risk of colon cancer with regular use of NSAIDs (7–9). A pro-inflammatory diet also may be linked to increased colon cancer risk through some component of the metabolic syndrome, especially insulin resistance or glucose intolerance (52, 53). We previously found that a higher DII score was associated with glucose intolerance among police officers in the Buffalo Cardio-Metabolic Occupational Police Stress study (54). Glucose intolerance and insulin resistance may lead to colorectal cancer through the growth-promoting effects of elevated levels of insulin, glucose, or triglycerides (53).
Strengths of the current study include a large, well-characterized population of more than 150,000 women, a long follow-up period, the inclusion of women of diverse race/ethnic groups, and the central adjudication of colorectal cancer diagnosis. The use of a novel dietary index to score diet quality based on inflammatory potential supports the evidence linking inflammation and colorectal cancer. Limitations include known measurement error in using an FFQ for dietary assessment such as under-reporting of energy and protein (29, 30) which may have led to underestimation of the impact of pro-inflammatory diets on colorectal cancer in this study. Changes in inflammatory potential of diet over time were not accounted for because only baseline dietary data were used to calculate the DII. It also is important to note that components missing from the FFQ, including ginger, turmeric, garlic, oregano, pepper, rosemary, eugenol, saffron, flavan-3-ol, flavones, flavonols, flavonones, anthocyanidins, are strongly anti-inflammatory. Even though we showed previously that reasonable predictive ability was retained when replacing 24-hour recall-derived DII scores with those derived from a structured questionnaire with fewer DII components (21), there still may be a fall-off in predictive ability in a population that was actively trying to change to a more healthful diet and therefore might be more likely to begin consuming these food items that are not on the FFQ list. Finally, the smaller number of incident cases of distal colon cancer and rectal cancer may have limited our ability to observe an association with the DII in this study.
CONCLUSION
Consuming a diet with high pro-inflammatory potential is associated with an increased risk of colorectal cancer, especially cancer of the proximal colon. This finding strengthens the evidence for a new tool assessing the overall inflammatory capacity of diet and suggests reduction in the inflammatory potential of the diet as a target for future intervention studies aimed at colon cancer prevention.
Acknowledgments
The Prevent Cancer Foundation Living in Pink grant supported Drs. Susan Steck and Jiajia Zhang, The University of South Carolina SPARC grant supported Dr. Fred Tabung, while the National Institutes of Health/ National Cancer Institute provided support to Dr. James Hebert (via U54 CA153461 and K05 CA136975). Dr. Yunsheng Ma was partly supported by grant Nos. 1R21 DK083700-01A1 and 1R01HL094575-01A1. The National Institutes of Health funded the WHI program through contracts HHSN268201100046C, HHSN268201100001C, HHSN268201100002C, HHSN268201100003C, HHSN268201100004C, and HHSN271201100004C.
APPENDIX SHORT LIST OF WHI INVESTIGATORS
Program Office: (National Heart, Lung, and Blood Institute, Bethesda, Maryland) Jacques Rossouw, Shari Ludlam, Dale Burwen, Joan McGowan, Leslie Ford, and Nancy Geller
Clinical Coordinating Center: (Fred Hutchinson Cancer Research Center, Seattle, WA) Garnet Anderson, Ross Prentice, Andrea LaCroix, and Charles Kooperberg
Investigators and Academic Centers: (Brigham and Women’s Hospital, Harvard Medical School, Boston, MA) JoAnn E. Manson; (MedStar Health Research Institute/Howard University, Washington, DC) Barbara V. Howard; (Stanford Prevention Research Center, Stanford, CA) Marcia L. Stefanick; (The Ohio State University, Columbus, OH) Rebecca Jackson; (University of Arizona, Tucson/Phoenix, AZ) Cynthia A. Thomson; (University at Buffalo, Buffalo, NY) Jean Wactawski-Wende; (University of Florida, Gainesville/Jacksonville, FL) Marian Limacher; (University of Iowa, Iowa City/Davenport, IA) Robert Wallace; (University of Pittsburgh, Pittsburgh, PA) Lewis Kuller; (Wake Forest University School of Medicine, Winston-Salem, NC) Sally Shumaker
Women’s Health Initiative Memory Study: (Wake Forest University School of Medicine, Winston-Salem, NC) Sally Shumaker
Footnotes
CONFLICT OF INTEREST
All authors declare that they have no conflict of interest
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