Background: Chronic inflammation and oxidative stress are thought to be involved in colorectal cancer development. These processes may contribute to leakage of bacterial products, such as lipopolysaccharide (LPS) and flagellin, across the gut barrier. The objective of this study, nested within a prospective cohort, was to examine associations between circulating LPS and flagellin serum antibody levels and colorectal cancer risk.

Methods: A total of 1,065 incident colorectal cancer cases (colon, n = 667; rectal, n = 398) were matched (1:1) to control subjects. Serum flagellin- and LPS-specific IgA and IgG levels were quantitated by ELISA. Multivariable conditional logistic regression models were used to calculate ORs and 95% confidence intervals (CI), adjusting for multiple relevant confouding factors.

Results: Overall, elevated anti-LPS and anti-flagellin biomarker levels were not associated with colorectal cancer risk. After testing potential interactions by various factors relevant for colorectal cancer risk and anti-LPS and anti-flagellin, sex was identified as a statistically significant interaction factor (Pinteraction < 0.05 for all the biomarkers). Analyses stratified by sex showed a statistically significant positive colorectal cancer risk association for men (fully-adjusted OR for highest vs. lowest quartile for total anti-LPS + flagellin, 1.66; 95% CI, 1.10–2.51; Ptrend, 0.049), whereas a borderline statistically significant inverse association was observed for women (fully-adjusted OR, 0.70; 95% CI, 0.47–1.02; Ptrend, 0.18).

Conclusion: In this prospective study on European populations, we found bacterial exposure levels to be positively associated to colorectal cancer risk among men, whereas in women, a possible inverse association may exist.

Impact: Further studies are warranted to better clarify these preliminary observations. Cancer Epidemiol Biomarkers Prev; 25(2); 291–301. ©2016 AACR.

This article is featured in Highlights of This Issue, p. 227

Colorectal cancer is one of the most commonly diagnosed cancers and a leading cause of death worldwide (1). It has been postulated that dietary and metabolic factors, such as energy excess and obesity, can cause breakdown of the colonic epithelial barrier function, allowing the interaction of innate immune system with bacterial products, such as lipopolysaccharide (LPS), also known as endotoxin (2). The human gastrointestinal (GI) tract is colonized by a complex community of approximately 1014 commensal bacteria, representing approximately 1,000 species (3). Colonic microbiota are being increasingly recognized as important contributors to GI health and likely also to colorectal cancer development (4).

LPS is an integral part of the outer membrane of gram-negative bacterial cell wall and also has a major role in both acute and chronic inflammation (5). A related bacterial product is flagellin, the primary structural component of flagella and a dominant target of humoral immunity in response to infection (6). Emerging evidence suggests that an overabundance of bacterial LPS from the gut microbiota may trigger chronic inflammation and increased production of proinflammatory cytokines and increased reactive oxygen species (2, 7). These proinflammatory cytokines can activate the nuclear factor κβ (NF-κβ) pathway, which has been implicated in cell proliferation and DNA damage leading to carcinogenesis (8). Chronic inflammation has been associated with increased risk of colorectal cancer by several studies (9). Thus, hypothetically, long-term exposure to the localized inflammatory responses resulting from LPS exposure may promote colorectal cancer development.

Direct in-vivo measurement of LPS and flagellin levels is challenging, in part because their appearance in blood and organs is sporadic and partly because their presence is quite transient. Hence, a few recent studies have measured levels of immunoglobulins against LPS and flagellin, whose levels can persist for months following exposure to these products, in an attempt to broadly assess systemic exposure to these gut microbial products and probe their potential associations with various disease states (10, 11). In a recent study by Ziegler and colleagues (10), flagellin- and LPS-specific serum immunoglobulin levels (IgM, IgA, and IgG) were markedly increased in patients with short bowel syndrome (SBS) compared with healthy controls. In another study, IgA and IgG antibodies specific for flagellin monomers were shown to be a target of the elevated adaptive immune response associated with Crohn disease, a chronic inflammatory disease of the GI tract (12). Another line of evidence has emerged from a recent animal study that explored the intricate relationship between intestinal barrier function, microbial environment, and inflammation in colorectal cancer by demonstrating that an inflammatory microenvironment promotes colorectal cancer progression in mice (13). The study highlighted that defective intestinal barrier function at tumour sites facilitates invasion of microbial products, triggering inflammation and subsequent tumor growth.

Although the role of microbiota in development of colorectal carcinogenesis has been explored in basic science and animal studies (13, 14), there is currently no direct epidemiologic evidence for the role of endotoxemia and gut barrier dysfunction in colorectal cancer etiology. In the present study, we aimed to examine the association between serum LPS- and flagellin-specific immunoglobulin levels (IgA and IgG) and risk of colorectal cancer development within a nested case–control study in the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort.

Study population and data collection

We used a case–control design nested within the EPIC cohort, a large prospective cohort study with over 520,000 subjects enrolled from 23 centers in 10 Western European countries (Denmark, France, Greece, Germany, Italy, the Netherlands, Norway, Spain, Sweden, and United Kingdom). Details of the design and methods of the EPIC study, including information on dietary assessment methods, blood collection protocols, and follow-up procedures, have been previously described (15). Briefly, individuals who were eligible for the study were selected from the general population of a specific geographical area, town, or province. Exceptions included the French subcohort, which is based on members of the health insurance system or state-school employees, and the Utrecht (Netherlands) subcohort, which is based on women who underwent screening for breast cancer. Between 1992 and 1998, standardized lifestyle and personal history questionnaires, anthropometric data, and blood samples were collected from most participants at recruitment. Diet over the previous 12 months was assessed at recruitment by validated country-specific questionnaires designed to ensure high compliance and improved measures of local dietary habits (16).

In each of the study centers, fasting or nonfasting blood samples were drawn from participants who provided a blood sample and stored at 5°C to 10°C, protected from light, and transported to local laboratories for processing and aliquoting as previously described (15, 16). In all countries, except Denmark and Sweden, blood was separated in the local EPIC centeres and stored at the International Agency for Research on Cancer (Lyon, France; −196°C, nitrogen vapor). In Denmark, blood samples were stored locally at −150°C under nitrogen vapor. In Sweden, samples were stored in −80°C freezers.

Follow-up for cancer incidence and vital status

Vital status follow-up (98.4% complete) is collected by record linkage with regional and/or national mortality registries in all countries except Germany and Greece, and the Italian center of Naples, where data are collected actively. Incident cancer cases were determined through record linkage with regional cancer registries (Denmark, other Italian centers, the Netherlands, Norway, Spain, Sweden, and United Kingdom; completed up to June 2003) or via a combination of methods, including linkage with health insurance records, contacts with cancer and pathology registries, and active follow-up through study subjects or their next-of-kin (France, Germany, and Greece; completed up to June 2002). Follow-up began at the date of enrollment and ended at the date of colorectal cancer diagnosis.

Nested case–control study design and selection of study subjects

Case ascertainment and selection.

Eligible colorectal cancer cases were first incident, histologically confirmed cases diagnosed within the EPIC study population. Colon cancers were defined as tumors in the cecum, appendix, ascending colon, hepatic flexure, transverse colon, splenic flexure, and descending and sigmoid (C18.0–C18.7, according to the 10th Revision of the International Statistical Classification of Diseases, Injury, and Cause of Death), as well as tumors that were overlapping or unspecified (C18.8 and C18.9). Rectal cancers were defined as tumors occurring at the rectosigmoid junction (C19) or rectum (C20). Subjects with anal canal tumors were excluded from the study. Colorectal cancer is defined as a combination of the colon and rectal cancer cases. After exclusions of 23 subjects with missing laboratory measurements of LPS or flagellin and 49 subjects with incomplete matching, a total of 1,065 incident colorectal cancer cases (colon, n = 667; rectal, n = 398) with available biomarker measurements were included in the study.

Control selection.

For each identified cancer case, one control was matched by incidence density sampling by age (within 2.5 years), gender, administrative center, time of the day at blood collection, and fasting status at the time of blood collection (less than 3 hours, 3–6 hours, and more than 6 hours). Women were additionally matched on menopausal status (premenopausal, perimenopausal, postmenopausal, or surgically menopausal). Premenopausal women were further matched on phase of the menstrual cycle at blood collection, and postmenopausal women were matched on current use of hormone replacement therapy. Controls were defined as free of cancer, except nonmelanoma skin cancer, at the time of diagnosis of the case.

Laboratory biomarker measures for serum anti–flagellin- and anti–LPS-specific immunoglobulins

Serum anti–LPS- and anti–flagellin-specific IgA and IgG levels were quantitated by ELISA at Georgia State University as previously described (10, 11). Briefly, microtiter plates (DYNEX) were coated overnight with purified laboratory-made flagellin (100 ng/well) or purified E.coli LPS (2 μg/well; from E. coli 0128: B12, Sigma; catolog No. 2887) in 9.6 pH bicarbonate buffer. Serum samples from cases and controls diluted at a ratio of 1:200 were applied to wells coated with flagellin or LPS. After incubation and washing, the wells were incubated either with IgG coupled to horseradish peroxidase (GE; catalog No. 375112) or, in the case of IgA-specific antibodies, with peroxidase-labeled IgA (KPL; catolog No. 14-10-01). Quantitation of total immunoglobulins was performed using the colorimetric peroxidase substrate tetramethylbenzidine (TMZ), and optical density (OD) was read at 450 nm and 540 nm (the difference was taken to compensate for optical interference from the plate), with an ELISA plate reader. Data are reported as OD corrected by subtracting background (determined by readings in blank samples) and are normalized to each plate's control sample, which was prepared in bulk, aliquoted, frozen, and thawed daily as used. Only adjusted ODs were used in the analysis. Standardization was performed using preparations of known concentrations of IgA and IgG. Because previously performed assays for these biomarkers in replicates had a very low intraassay coefficient of variation (<5%; ref. 17), our samples were analyzed in singleton to minimize biosample volume requirement, cost, and time. Interassay coefficients of variation were between 3.8% and 6.8%. For all analyses, cases and matched controls were run in the same batch, and the case–control status of the samples was blinded to laboratory technicians.

In the present study, secondary use was made of relevant biomarker measures that had been conducted previously on the same series of subjects (18–20). Briefly, measurements of glycated hemoglobin (HbA1c) were done on erythrocyte hemolysate using the high-performance liquid chromatography method (Bio-Rad Variant II instrument; Bio Lad Laboratories) with intrabatch coefficient of variations of 2.5% (18). High-sensitivity C-reactive protein (hs-CRP) concentrations were measured using a high-sensitivity assay (Beckman-Coulter) on a Synchron LX-20 Pro autoanalyzer (Beckman-Coulter). The interassay coefficients of variation were 6.0% to 6.5% at various concentrations of hs-CRP (19).

Statistical analysis

The distributions of selected characteristics between colon and rectal cases and the matched controls were compared. Normality of each biomarker was checked by visual inspection, and all were deemed to be approximately normal. Each individual biomarker, as well as anti-flagellin (flagellin IgA+flagellin IgG), anti-LPS (LPS IgA+LPS IgG), and anti-flagellin+LPS exposure (flagellin IgA + flagellin IgG + LPS IgA + LPS IgG) levels were categorized into quartiles based on the distribution among the controls with the lowest quartile as the reference category.

Conditional logistic regression was used to estimate the ORs and 95% confidence intervals (CI) of colorectal cancer, and by anatomical subsite of cancers of colon and rectum in relation to levels of each circulating biomarkers. Risk estimates were computed from both univariate analyses adjusted for the matching factors (matching-adjusted) and multivariable analyses, with additional adjustments for established confounding variables (fully-adjusted), including smoking status (status/duration/intensity of smoking), body mass index (BMI, kg/m2), waist circumference (cm), education level, total alcohol consumption (g/d), physical activity (sex-specific combined total physical activity index), total energy intake (kcal/day), and total daily intakes of fiber (g/day), fruits and vegetables (g/day), and red/processed meats (g/day; refs. 21–26). For all models, collinearity was assessed, and tests for linear trend were performed using a score variable with values from 1 to 4 included in the model, consistent with the quartile grouping.

We evaluated interactions by several factors relevant for colorectal cancer risk that may be also related to anti–LPS- and anti–flagellin-IgA and -IgG concentrations, total bacterial load, and/or to colonic barrier function (27). Sex and tumor location (colon, rectum) were proposed a priori as potential interactions so results are presented stratified by these factors, as well as combined. Other variables (i.e., hs-CRP, waist circumference, BMI, dietary fat, and alcohol intake) were studied for hypothesis generation analyses. Continuous analyses were conducted using a cross-product term of each biomarker and potential interaction term in the model, followed by a likelihood ratio test. Discrete analyses were also undertaken for hs-CRP, waist circumference, BMI, dietary fat, and alcohol intake by including an interaction term formed by the product of the total anti-flagellin+LPS tertile (cutoff points: <5.58, 5.58 to <7.19, ≥7.19) and the sex-specific dichotomized high and low categories of the potential interaction. As with continuous analysis, a likelihood ratio test was used to assess statistical significance.

As a sensitivity analysis, we repeated the main multivariable-adjusted models after excluding cases that occurred in the first 2 years of follow-up and their matched controls to avoid possible reverse causality, as well as after exclusion of countries with lowest (Denmark) and highest (Greece) anti–LPS- and anti-flagellin exposure levels.

Conditional logistic–restricted cubic spline models were used to explore possible deviation from linear relationships between each biomarker and colorectal cancer, with four knots specific at the median of each quartile of biomarker levels (28).

A two-tailed P value of <0.05 was considered to be statistically significant. All statistical analyses were performed with SAS version 9.3 (SAS Institute) statistical software package.

Baseline characteristics of cases and controls

Selected baseline characteristics of the colon and rectal cases and their matched controls are compared in Table 1. Colon and rectal cancer cases were on average 58.8 years and 58.1 years old, respectively. Both colon and rectal cancer cases were more likely to be current smokers, inactive, had higher education, higher total daily energy, and consume less fruit and vegetable than their matched controls. For colon cancer, female cases had lower median concentrations of anti–flagellin-IgA (1.05 vs. 1.17), anti–LPS-IgA (1.56 vs. 1.71), and anti–LPS-IgG (1.36 vs. 1.44) than their matched controls, whereas the concentrations of each of these serologic biomarkers were higher in cases than controls in men (anti–flagellin-IgA: 1.33 vs. 1.30; anti–LPS-IgA: 1.83 vs. 1.68; anti–LPS-IgG: 1.45 vs. 1.37). For rectal cancer, concentrations of each of the serologic biomarkers were higher in cases than controls in women, except anti–LPS-IgA, where cases had lower concentrations than controls (1.38 vs. 1.48). On the other hand, concentrations of all the serologic biomarkers were slightly lower in male rectal cancer cases than controls, except anti–flagellin-IgA, where cases had higher concentrations than controls (1.29 vs. 1.17).

Table 1.

Baseline characteristics of incident colon and rectal cancer cases and matched controls in the EPIC cohort

Colon cancerRectal cancer
CharacteristicsCasesControlsCasesControls
Number 667 667 398 398 
Age, years, mean (SD) 
 At recruitment 58.8 (7.2) 58.8 (7.3) 58.1 (6.9) 58.0 (6.9) 
 At blood collection 59.0 (7.3) 59.0 (7.3) 58.1 (6.8) 58.1 (6.8) 
Women, n (%) 369 (55.3) 369 (55.3) 187 (47.0) 187 (47.0) 
BMI, kg/m2, mean (SD) 26.8 (4.5) 26.3 (3.9) 26.6 (4.1) 26.4 (3.9) 
Waist circumference, cm, mean (SD) 90.4 (13.2) 88.0 (12.2) 90.3 (13.1) 89.5 (13.1) 
Waist/hip ratio, mean (SD) 0.9 (0.1) 0.9 (0.1) 0.9 (0.1) 0.9 (0.1) 
Smoking status/duration/intensity, n (%) 
 Never-smoker 277 (41.5) 297 (44.5) 155 (38.9) 160 (40.2) 
 Ex-smokers, duration of smoking < 10 years 40 (6.0) 43 (6.5) 21 (5.3) 30 (7.5) 
 Ex-smokers, duration of smoking ≥ 10 years 165 (24.7) 164 (24.6) 104 (26.1) 91 (22.9) 
 Ex-smokers, missing duration of smoking 16 (2.4) 13 (1.9) 4 (1.0) 8 (2.0) 
 Smokers, <15 cigarettes a day 109 (16.3) 97 (14.5) 79 (19.9) 63 (15.8) 
 Smokers, ≥15 to <25 cigarettes a day 43 (6.5) 39 (5.9) 24 (6.0) 35 (8.8) 
 Smokers, ≥25 cigarettes a day 9 (1.4) 6 (0.9) 8 (2.0) 5 (1.3) 
 Missing smoking status 8 (1.2) 8 (1.2) 3 (0.8) 6 (1.5) 
Physical activity, n (%) 
 Inactive 107 (16.0) 78 (11.7) 59 (14.8) 58 (14.6) 
 Moderately inactive 202 (30.3) 210 (31.5) 116 (29.2) 103 (25.9) 
 Moderately active 292 (43.8) 296 (44.4) 176 (44.2) 167 (42.0) 
 Active 62 (9.3) 77 (11.5) 47 (11.8) 61 (15.3) 
 Missing/unspecified 4 (0.6) 6 (0.9) 9 (2.2) 
Education, % 
 None/primary school 259 (39.1) 292 (44.0) 150 (38.0) 163 (41.2) 
 Technical/professional school 158 (23.8) 161 (24.2) 106 (26.8) 109 (27.5) 
 Secondary school 111 (16.7) 87 (13.1) 54 (13.7) 43 (10.9) 
 University or higher 117 (17.6) 109 (16.4) 76 (19.2) 76 (19.2) 
 Missing/unspecified 18 (2.7) 15 (2.3) 9 (2.3) 5 (1.2) 
Premenopausal women, n (%) 41 (11.1) 42 (11.4) 16 (8.6) 16 (8.6) 
Hormone replacement therapy use, n (%) 42 (11.5) 40 (10.9) 19 (10.3) 19 (10.3) 
Alcohol consumption, g/d, median (IQR) 8.6 (1.3–22.4) 8.4 (1.5–21.1) 11.6 (2.4–31.5) 10.5 (2.2–25.2) 
Dietary intakes 
 Total energy, kcal/d, median (IQR) 2,066.5 (1,693.2–2,505.2) 2,058.7 (1,729.2–2,453.1) 2,158.6 (1,726.7–2,568.8) 2,093.8 (1,721.3–2,537.8) 
 Total fats, g/d, median (IQR) 77.4 (60.4–97.5) 77.1 (61.8–97.0) 79.2 (60.2–103.4) 79.6 (63.0–100.5) 
 Fiber intake, g/d, median (IQR) 22.2 (17.2–27.3) 23.0 (18.4–27.4) 22.0 (18.2–27.7) 22.8 (17.8–28.3) 
 Fruit and vegetable intake, g/d, median (IQR) 368.9 (244.73–523.9) 417.0 (267.3–566.0) 361.9 (247.8–503.3) 369.9 (251.7–534.4) 
 Fish and shellfish intake, g/d, median (IQR) 27.0 (14.8–46.7) 29.0 (14.5–50.3) 28.0 (16.0–51.3) 30.0 (14.0–51.5) 
 Red meat intake, g/d, median (IQR) 48.3 (25.5–77.1) 48.3 (25.8–76.5) 55.2 (33.6–83.1) 54.0 (31.7–81.6) 
 Processed meat intake, g/d, median (IQR) 25.0 (13.0–40.8) 23.4 (12.5–41.6) 27.3 (13.9–47.5) 26.4 (13.0–46.5) 
Fasting status, % 
 Yes 25.5 25.5 18.6 18.6 
 No 48.9 48.9 57.9 57.9 
 In between 25.6 25.6 23.4 23.4 
Blood biomarkers 
 Hs-CRP, mg/L, median (IQR)     
  Men 2.81 (1.25–5.15) 1.96 (0.89–4.26) 2.13 (1.00–4.31) 2.16 (0.98–4.21) 
  Women 3.36 (1.28–5.88) 2.59 (1.25–5.11) 2.60 (1.00–4.72) 2.56 (1.09–4.20) 
 Cholesterol, mmol/L, median (IQR) 
  Men 6.07 (5.24–6.86) 6.21 (5.54–6.90) 6.27 (5.57–7.05) 6.22 (5.52–7.00) 
  Women 6.43 (5.65–7.30) 6.61 (5.77–7.42) 6.55 (5.70–7.30) 6.80 (6.08–7.67) 
 HDL, mmol/L, median (IQR) 
  Men 1.23 (1.04–1.48) 1.29 (1.09–1.60) 1.28 (1.09–1.56) 1.27 (1.07–1.52) 
  Women 1.51 (1.25–1.78) 1.53 (1.29–1.90) 1.63 (1.33–1.86) 1.61 (1.33–1.86) 
 LDL, mmol/L, median (IQR) 
  Men 3.99 (3.42–4.72) 4.17 (3.57–4.70) 4.16 (3.55–4.86) 4.23 (3.41–4.90) 
  Women 4.24 (3.56–5.03) 4.30 (3.55–5.06) 4.23 (3.43–4.85) 4.31 (3.70–5.33) 
 Glycated hemoglobin mg/L, median (IQR) 
  Men 5.7 (5.5–6.1) 5.7 (5.5–6.0) 5.7 (5.5–6.0) 5.8 (5.5–6.1) 
  Women 5.8 (5.5–6.1) 5.7 (5.5–5.9) 5.8 (5.5–6.0) 5.6 (5.5–5.9) 
 Anti–flagellin-IgA, OD, median (IQR) 
  Men 1.33 (0.94–1.79) 1.30 (0.92–1.78) 1.29 (0.85–1.68) 1.17 (0.84–1.67) 
  Women 1.05 (0.76–1.52) 1.17 (0.80–1.67) 1.01 (0.68–1.47) 0.95 (0.70–1.45) 
 Anti–flagellin-IgG, OD, median (IQR) 
  Men 1.97 (1.42–2.55) 1.99 (1.42–2.51) 1.88 (1.31–2.53) 1.92 (1.43–2.61) 
  Women 2.03 (1.50–2.63) 2.00 (1.50–2.64) 2.10 (1.48–2.61) 2.02 (1.46–2.58) 
 Anti–LPS-IgA, OD, median (IQR) 
  Men 1.83 (1.29–2.41) 1.68 (1.28–2.14) 1.62 (1.22–2.26) 1.66 (1.24–2.03) 
  Women 1.56 (1.19–2.16) 1.71 (1.22–2.24) 1.38 (1.06–1.87) 1.48 (1.04–1.94) 
 Anti–LPS-IgG, OD, median (IQR) 
  Men 1.45 (1.06–1.91) 1.37 (1.08–1.83) 1.28 (1.01–1.85) 1.35 (1.00–1.85) 
  Women 1.36 (1.00–1.83) 1.44 (1.09–1.93) 1.43 (1.08–1.82) 1.32 (1.01–1.73) 
Colon cancerRectal cancer
CharacteristicsCasesControlsCasesControls
Number 667 667 398 398 
Age, years, mean (SD) 
 At recruitment 58.8 (7.2) 58.8 (7.3) 58.1 (6.9) 58.0 (6.9) 
 At blood collection 59.0 (7.3) 59.0 (7.3) 58.1 (6.8) 58.1 (6.8) 
Women, n (%) 369 (55.3) 369 (55.3) 187 (47.0) 187 (47.0) 
BMI, kg/m2, mean (SD) 26.8 (4.5) 26.3 (3.9) 26.6 (4.1) 26.4 (3.9) 
Waist circumference, cm, mean (SD) 90.4 (13.2) 88.0 (12.2) 90.3 (13.1) 89.5 (13.1) 
Waist/hip ratio, mean (SD) 0.9 (0.1) 0.9 (0.1) 0.9 (0.1) 0.9 (0.1) 
Smoking status/duration/intensity, n (%) 
 Never-smoker 277 (41.5) 297 (44.5) 155 (38.9) 160 (40.2) 
 Ex-smokers, duration of smoking < 10 years 40 (6.0) 43 (6.5) 21 (5.3) 30 (7.5) 
 Ex-smokers, duration of smoking ≥ 10 years 165 (24.7) 164 (24.6) 104 (26.1) 91 (22.9) 
 Ex-smokers, missing duration of smoking 16 (2.4) 13 (1.9) 4 (1.0) 8 (2.0) 
 Smokers, <15 cigarettes a day 109 (16.3) 97 (14.5) 79 (19.9) 63 (15.8) 
 Smokers, ≥15 to <25 cigarettes a day 43 (6.5) 39 (5.9) 24 (6.0) 35 (8.8) 
 Smokers, ≥25 cigarettes a day 9 (1.4) 6 (0.9) 8 (2.0) 5 (1.3) 
 Missing smoking status 8 (1.2) 8 (1.2) 3 (0.8) 6 (1.5) 
Physical activity, n (%) 
 Inactive 107 (16.0) 78 (11.7) 59 (14.8) 58 (14.6) 
 Moderately inactive 202 (30.3) 210 (31.5) 116 (29.2) 103 (25.9) 
 Moderately active 292 (43.8) 296 (44.4) 176 (44.2) 167 (42.0) 
 Active 62 (9.3) 77 (11.5) 47 (11.8) 61 (15.3) 
 Missing/unspecified 4 (0.6) 6 (0.9) 9 (2.2) 
Education, % 
 None/primary school 259 (39.1) 292 (44.0) 150 (38.0) 163 (41.2) 
 Technical/professional school 158 (23.8) 161 (24.2) 106 (26.8) 109 (27.5) 
 Secondary school 111 (16.7) 87 (13.1) 54 (13.7) 43 (10.9) 
 University or higher 117 (17.6) 109 (16.4) 76 (19.2) 76 (19.2) 
 Missing/unspecified 18 (2.7) 15 (2.3) 9 (2.3) 5 (1.2) 
Premenopausal women, n (%) 41 (11.1) 42 (11.4) 16 (8.6) 16 (8.6) 
Hormone replacement therapy use, n (%) 42 (11.5) 40 (10.9) 19 (10.3) 19 (10.3) 
Alcohol consumption, g/d, median (IQR) 8.6 (1.3–22.4) 8.4 (1.5–21.1) 11.6 (2.4–31.5) 10.5 (2.2–25.2) 
Dietary intakes 
 Total energy, kcal/d, median (IQR) 2,066.5 (1,693.2–2,505.2) 2,058.7 (1,729.2–2,453.1) 2,158.6 (1,726.7–2,568.8) 2,093.8 (1,721.3–2,537.8) 
 Total fats, g/d, median (IQR) 77.4 (60.4–97.5) 77.1 (61.8–97.0) 79.2 (60.2–103.4) 79.6 (63.0–100.5) 
 Fiber intake, g/d, median (IQR) 22.2 (17.2–27.3) 23.0 (18.4–27.4) 22.0 (18.2–27.7) 22.8 (17.8–28.3) 
 Fruit and vegetable intake, g/d, median (IQR) 368.9 (244.73–523.9) 417.0 (267.3–566.0) 361.9 (247.8–503.3) 369.9 (251.7–534.4) 
 Fish and shellfish intake, g/d, median (IQR) 27.0 (14.8–46.7) 29.0 (14.5–50.3) 28.0 (16.0–51.3) 30.0 (14.0–51.5) 
 Red meat intake, g/d, median (IQR) 48.3 (25.5–77.1) 48.3 (25.8–76.5) 55.2 (33.6–83.1) 54.0 (31.7–81.6) 
 Processed meat intake, g/d, median (IQR) 25.0 (13.0–40.8) 23.4 (12.5–41.6) 27.3 (13.9–47.5) 26.4 (13.0–46.5) 
Fasting status, % 
 Yes 25.5 25.5 18.6 18.6 
 No 48.9 48.9 57.9 57.9 
 In between 25.6 25.6 23.4 23.4 
Blood biomarkers 
 Hs-CRP, mg/L, median (IQR)     
  Men 2.81 (1.25–5.15) 1.96 (0.89–4.26) 2.13 (1.00–4.31) 2.16 (0.98–4.21) 
  Women 3.36 (1.28–5.88) 2.59 (1.25–5.11) 2.60 (1.00–4.72) 2.56 (1.09–4.20) 
 Cholesterol, mmol/L, median (IQR) 
  Men 6.07 (5.24–6.86) 6.21 (5.54–6.90) 6.27 (5.57–7.05) 6.22 (5.52–7.00) 
  Women 6.43 (5.65–7.30) 6.61 (5.77–7.42) 6.55 (5.70–7.30) 6.80 (6.08–7.67) 
 HDL, mmol/L, median (IQR) 
  Men 1.23 (1.04–1.48) 1.29 (1.09–1.60) 1.28 (1.09–1.56) 1.27 (1.07–1.52) 
  Women 1.51 (1.25–1.78) 1.53 (1.29–1.90) 1.63 (1.33–1.86) 1.61 (1.33–1.86) 
 LDL, mmol/L, median (IQR) 
  Men 3.99 (3.42–4.72) 4.17 (3.57–4.70) 4.16 (3.55–4.86) 4.23 (3.41–4.90) 
  Women 4.24 (3.56–5.03) 4.30 (3.55–5.06) 4.23 (3.43–4.85) 4.31 (3.70–5.33) 
 Glycated hemoglobin mg/L, median (IQR) 
  Men 5.7 (5.5–6.1) 5.7 (5.5–6.0) 5.7 (5.5–6.0) 5.8 (5.5–6.1) 
  Women 5.8 (5.5–6.1) 5.7 (5.5–5.9) 5.8 (5.5–6.0) 5.6 (5.5–5.9) 
 Anti–flagellin-IgA, OD, median (IQR) 
  Men 1.33 (0.94–1.79) 1.30 (0.92–1.78) 1.29 (0.85–1.68) 1.17 (0.84–1.67) 
  Women 1.05 (0.76–1.52) 1.17 (0.80–1.67) 1.01 (0.68–1.47) 0.95 (0.70–1.45) 
 Anti–flagellin-IgG, OD, median (IQR) 
  Men 1.97 (1.42–2.55) 1.99 (1.42–2.51) 1.88 (1.31–2.53) 1.92 (1.43–2.61) 
  Women 2.03 (1.50–2.63) 2.00 (1.50–2.64) 2.10 (1.48–2.61) 2.02 (1.46–2.58) 
 Anti–LPS-IgA, OD, median (IQR) 
  Men 1.83 (1.29–2.41) 1.68 (1.28–2.14) 1.62 (1.22–2.26) 1.66 (1.24–2.03) 
  Women 1.56 (1.19–2.16) 1.71 (1.22–2.24) 1.38 (1.06–1.87) 1.48 (1.04–1.94) 
 Anti–LPS-IgG, OD, median (IQR) 
  Men 1.45 (1.06–1.91) 1.37 (1.08–1.83) 1.28 (1.01–1.85) 1.35 (1.00–1.85) 
  Women 1.36 (1.00–1.83) 1.44 (1.09–1.93) 1.43 (1.08–1.82) 1.32 (1.01–1.73) 

NOTE: Cases and controls were matched on age (within 2.5 years), gender, administrative center, hormone therapy, fasting status, and date of blood collection (within 45 days).

Abbreviations: HDL, high-density lipoprotein; IgA, immunoglobulin A; IgG, immunoglobulin G; IQR, interquartile range; LDL, low-density lipoprotein.

Associations of anti–flagellin- and anti–LPS-IgA and IgG with colorectal cancer

All models found no association between colorectal cancer and biomarkers of either anti-LPS or anti-flagellin (Supplementary Table S1). However, when analyses were stratified by sex, a significant interaction of the colorectal cancer-LPS/flagellin risk association (total anti-flagellin+LPS, Pinteraction < 0.05) was observed. Among men, there was a significant, positive association between colorectal cancer risk and levels of total anti-flagellin+LPS exposure (Table 2) with a fully-adjusted OR of 1.66 (95% CI, 1.10–2.51) comparing the higest versus lowest quartiles, and a significant test for trend (Ptrend 0.049). In contrast, among women, there were inverse associations with colorectal cancer risk. Anti–flagellin-IgA was negatively associated with risk of colorectal cancer (fully-adjusted OR, 0.65 comparing highest vs. lowest quartiles; 95% CI, 0.44–0.96; Ptrend = 0.02). In addition, there was a trend of significant inverse association between anti–LPS-IgA and risk of colorectal cancer with Ptrend of 0.02. Unlike among men, the levels of total anti-flagellin+LPS exposure were also negatively related to colorectal cancer risk, though the association did not reach significance (fully-adjusted OR, 0.70 comparing highest vs. lowest quartiles; 95% CI, 0.47–1.02; Ptrend = 0.18).

Table 2.

ORs (95% CI) for risk of colorectal cancer by quartile of baseline biomarkers of anti–LPS- and anti–flagellin-IgA and IgG: stratified by sex

ContinuousQuartilesa
Serum immunoglobulins against LPS and flagellin, OD(per 1-SD increase) OR (95% CI)Q1 ORQ2 OR (95% CI)Q3 OR (95% CI)Q4 OR (95% CI)Ptrendb
Men 
 Anti–Flic-IgA, no. Ca/Co 509/509 94/102 120/128 147/138 148/141  
  SD/cutoff point 0.72 ≤0.81 >0.81 to ≤ 1.18 >1.18 to ≤ 1.68 >1.68  
  Matching-adjusted modelc 1.03 (0.91–1.18) 1.00 1.01 (0.70–1.46) 1.17 (0.81–1.70) 1.16 (0.80–1.68) 0.35 
  Fully-adjusted modeld 1.01 (0.88–1.16) 1.00 1.07 (0.73–1.58) 1.26 (0.85–1.85) 1.16 (0.78–1.71) 0.39 
 Anti–Flic-IgG, no. Ca/Co 509/509 145/138 121/125 124/124 119/122  
  SD/cutoff point 0.78 ≤1.46 >1.46 to ≤ 1.98 >1.98 to ≤ 2.58 >2.58  
  Matching-adjusted modelc 0.96 (0.84–1.10) 1.00 0.92 (0.64–1.30) 0.94 (0.65–1.37) 0.92 (0.63–1.33) 0.69 
  Fully-adjusted modeld 0.99 (0.85–1.14) 1.00 0.96 (0.66–1.38) 0.96 (0.65–1.43) 0.99 (0.66–1.47) 0.95 
 Anti–LPS-IgA, no. Ca/Co 509/509 118/115 116/134 120/138 155/122  
  SD/cutoff point 0.72 ≤1.21 >1.21 to ≤ 1.66 >1.66 to ≤ 2.13 >2.13  
  Matching-adjusted modelc 1.18 (1.03–1.37) 1.00 0.84 (0.58–1.23) 0.87 (0.59–1.28) 1.32 (0.88–1.98) 0.14 
  Fully-adjusted modeld 1.17 (1.01–1.36) 1.00 0.82 (0.55–1.21) 0.82 (0.54–1.23) 1.26 (0.82–1.94) 0.27 
 Anti–LPS-IgG, no. Ca/Co 509/509 135/127 116/130 123/131 135/121  
  SD/cutoff point 0.61 ≤1.06 >1.06 to ≤ 1.36 >1.36 to ≤ 1.84 >1.84  
  Matching-adjusted modelc 1.08 (0.96–1.23) 1.00 0.83 (0.58–1.20) 0.88 (0.62–1.26) 1.05 (0.72–1.53) 0.72 
  Fully-adjusted modeld 1.12 (0.98–1.28) 1.00 0.85 (0.58–1.25) 0.88 (0.61–1.29) 1.13 (0.75–1.68) 0.51 
 Total anti-Flic, no. Ca/Co 509/509 129/130 113/115 123/133 144/131  
  SD/cutoff point 1.23 ≤2.47 >2.47 to ≤ 3.19 >3.19 to ≤ 4.05 >4.05  
  Matching-adjusted modelc 0.99 (0.87–1.13) 1.00 1.00 (0.70–1.41) 0.94 (0.66–1.34) 1.12 (0.78–1.62) 0.61 
  Fully-adjusted modeld 1.00 (0.87–1.15) 1.00 1.00 (0.70–1.44) 1.00 (0.70–1.45) 1.16 (0.79–1.71) 0.47 
 Total anti-LPS, no. Ca/Co 509/509 120/115 103/144 129/131 157/119  
  SD/cutoff point 1.11 ≤2.41 >2.41 to ≤ 3.04 >3.04 to ≤ 3.87 >3.87  
  Matching-adjusted modelc 1.17 (1.02–1.34) 1.00 0.71 (0.50–1.02) 1.01 (0.69–1.47) 1.41 (0.95–2.09) 0.04 
  Fully-adjusted modeld 1.18 (1.02–1.37) 1.00 0.71 (0.49–1.04) 0.98 (0.66–1.46) 1.42 (0.94–2.16) 0.04 
 Total anti-Flic & LPS, no. Ca/Co 509/509 107/127 128/124 123/135 151/123  
  SD/cutoff point 2.00 ≤5.13 >5.13 to ≤ 6.35 >6.35 to ≤ 7.73 >7.73  
  Matching-adjusted modelc 1.08 (0.95–1.24) 1.00 1.24 (0.87–1.76) 1.12 (0.79–1.59) 1.55 (1.06–2.27) 0.05 
  Fully-adjusted modeld 1.09 (0.95–1.26) 1.00 1.31 (0.90–1.90) 1.11 (0.77–1.61) 1.66 (1.10–2.51) 0.05 
Womene 
 Anti–Flic-IgA, no. Ca/Co 556/556 176/165 159/139 122/127 99/125  
  Matching-adjusted modelc 0.87 (0.76–1.00) 1.00 1.03 (0.76–1.41) 0.86 (0.61–1.21) 0.70 (0.49–1.02) 0.04 
  Fully-adjusted modeld 0.84 (0.73–0.98) 1.00 0.97 (0.70–1.35) 0.81 (0.57–1.16) 0.65 (0.44–0.96) 0.02 
 Anti–Flic-IgG, no. Ca/Co 556/556 131/129 139/141 138/142 148/144  
  Matching-adjusted modelc 0.96 (0.85–1.10) 1.00 0.97 (0.69–1.36) 0.96 (0.67–1.36) 1.01 (0.71–1.43) 0.95 
  Fully-adjusted modeld 0.98 (0.85–1.12) 1.00 1.01 (0.71–1.44) 1.06 (0.73–1.53) 1.05 (0.73–1.52) 0.74 
 Anti–LPS-IgA, no. Ca/Co 556/556 165/152 161/133 102/127 128/144  
  Matching-adjusted modelc 0.89 (0.78–1.01) 1.00 1.09 (0.78–1.55) 0.72 (0.50–1.03) 0.78 (0.54–1.11) 0.04 
  Fully-adjusted modeld 0.86 (0.75–0.99) 1.00 1.06 (0.74–1.53) 0.67 (0.46–0.98) 0.73 (0.50–1.06) 0.02 
 Anti–LPS-IgG, no. Ca/Co 556/556 152/141 115/135 158/135 131/145  
  Matching-adjusted modelc 0.95 (0.84–1.08) 1.00 0.78 (0.55–1.10) 1.09 (0.78–1.54) 0.83 (0.58–1.19) 0.64 
  Fully-adjusted modeld 0.94 (0.82–1.08) 1.00 0.87 (0.60–1.25) 1.11 (0.78–1.59) 0.83 (0.57–1.21) 0.61 
 Total anti-Flic, no. Ca/Co 556/556 147/137 136/151 147/132 126/136  
  Matching-adjusted modelc 0.90 (0.79–1.03) 1.00 0.83 (0.59–1.16) 1.02 (0.73–1.42) 0.84 (0.58–1.21) 0.67 
  Fully-adjusted modeld 0.89 (0.77–1.03) 1.00 0.83 (0.59–1.18) 1.09 (0.77–1.54) 0.83 (0.56–1.21) 0.72 
 Total anti-LPS, no. Ca/Co 556/556 162/152 129/122 137/134 128/148  
  Matching-adjusted modelc 0.90 (0.79–1.02) 1.00 0.98 (0.69–1.38) 0.94 (0.67–1.31) 0.77 (0.53–1.10) 0.17 
  Fully-adjusted modeld 0.88 (0.76–1.01) 1.00 1.01 (0.71–1.46) 0.91 (0.64–1.30) 0.74 (0.51–1.09) 0.12 
 Total anti-Flic & LPS, no. Ca/Co 556/556 153/140 139/141 144/132 120/143  
  Matching-adjusted modelc 0.88 (0.77–1.01) 1.00 0.90 (0.65–1.24) 0.99 (0.71–1.39) 0.73 (0.50–1.05) 0.17 
  Fully-adjusted modeld 0.86 (0.75–1.00) 1.00 0.89 (0.63–1.25) 1.05 (0.74–1.49) 0.70 (0.47–1.02) 0.18 
ContinuousQuartilesa
Serum immunoglobulins against LPS and flagellin, OD(per 1-SD increase) OR (95% CI)Q1 ORQ2 OR (95% CI)Q3 OR (95% CI)Q4 OR (95% CI)Ptrendb
Men 
 Anti–Flic-IgA, no. Ca/Co 509/509 94/102 120/128 147/138 148/141  
  SD/cutoff point 0.72 ≤0.81 >0.81 to ≤ 1.18 >1.18 to ≤ 1.68 >1.68  
  Matching-adjusted modelc 1.03 (0.91–1.18) 1.00 1.01 (0.70–1.46) 1.17 (0.81–1.70) 1.16 (0.80–1.68) 0.35 
  Fully-adjusted modeld 1.01 (0.88–1.16) 1.00 1.07 (0.73–1.58) 1.26 (0.85–1.85) 1.16 (0.78–1.71) 0.39 
 Anti–Flic-IgG, no. Ca/Co 509/509 145/138 121/125 124/124 119/122  
  SD/cutoff point 0.78 ≤1.46 >1.46 to ≤ 1.98 >1.98 to ≤ 2.58 >2.58  
  Matching-adjusted modelc 0.96 (0.84–1.10) 1.00 0.92 (0.64–1.30) 0.94 (0.65–1.37) 0.92 (0.63–1.33) 0.69 
  Fully-adjusted modeld 0.99 (0.85–1.14) 1.00 0.96 (0.66–1.38) 0.96 (0.65–1.43) 0.99 (0.66–1.47) 0.95 
 Anti–LPS-IgA, no. Ca/Co 509/509 118/115 116/134 120/138 155/122  
  SD/cutoff point 0.72 ≤1.21 >1.21 to ≤ 1.66 >1.66 to ≤ 2.13 >2.13  
  Matching-adjusted modelc 1.18 (1.03–1.37) 1.00 0.84 (0.58–1.23) 0.87 (0.59–1.28) 1.32 (0.88–1.98) 0.14 
  Fully-adjusted modeld 1.17 (1.01–1.36) 1.00 0.82 (0.55–1.21) 0.82 (0.54–1.23) 1.26 (0.82–1.94) 0.27 
 Anti–LPS-IgG, no. Ca/Co 509/509 135/127 116/130 123/131 135/121  
  SD/cutoff point 0.61 ≤1.06 >1.06 to ≤ 1.36 >1.36 to ≤ 1.84 >1.84  
  Matching-adjusted modelc 1.08 (0.96–1.23) 1.00 0.83 (0.58–1.20) 0.88 (0.62–1.26) 1.05 (0.72–1.53) 0.72 
  Fully-adjusted modeld 1.12 (0.98–1.28) 1.00 0.85 (0.58–1.25) 0.88 (0.61–1.29) 1.13 (0.75–1.68) 0.51 
 Total anti-Flic, no. Ca/Co 509/509 129/130 113/115 123/133 144/131  
  SD/cutoff point 1.23 ≤2.47 >2.47 to ≤ 3.19 >3.19 to ≤ 4.05 >4.05  
  Matching-adjusted modelc 0.99 (0.87–1.13) 1.00 1.00 (0.70–1.41) 0.94 (0.66–1.34) 1.12 (0.78–1.62) 0.61 
  Fully-adjusted modeld 1.00 (0.87–1.15) 1.00 1.00 (0.70–1.44) 1.00 (0.70–1.45) 1.16 (0.79–1.71) 0.47 
 Total anti-LPS, no. Ca/Co 509/509 120/115 103/144 129/131 157/119  
  SD/cutoff point 1.11 ≤2.41 >2.41 to ≤ 3.04 >3.04 to ≤ 3.87 >3.87  
  Matching-adjusted modelc 1.17 (1.02–1.34) 1.00 0.71 (0.50–1.02) 1.01 (0.69–1.47) 1.41 (0.95–2.09) 0.04 
  Fully-adjusted modeld 1.18 (1.02–1.37) 1.00 0.71 (0.49–1.04) 0.98 (0.66–1.46) 1.42 (0.94–2.16) 0.04 
 Total anti-Flic & LPS, no. Ca/Co 509/509 107/127 128/124 123/135 151/123  
  SD/cutoff point 2.00 ≤5.13 >5.13 to ≤ 6.35 >6.35 to ≤ 7.73 >7.73  
  Matching-adjusted modelc 1.08 (0.95–1.24) 1.00 1.24 (0.87–1.76) 1.12 (0.79–1.59) 1.55 (1.06–2.27) 0.05 
  Fully-adjusted modeld 1.09 (0.95–1.26) 1.00 1.31 (0.90–1.90) 1.11 (0.77–1.61) 1.66 (1.10–2.51) 0.05 
Womene 
 Anti–Flic-IgA, no. Ca/Co 556/556 176/165 159/139 122/127 99/125  
  Matching-adjusted modelc 0.87 (0.76–1.00) 1.00 1.03 (0.76–1.41) 0.86 (0.61–1.21) 0.70 (0.49–1.02) 0.04 
  Fully-adjusted modeld 0.84 (0.73–0.98) 1.00 0.97 (0.70–1.35) 0.81 (0.57–1.16) 0.65 (0.44–0.96) 0.02 
 Anti–Flic-IgG, no. Ca/Co 556/556 131/129 139/141 138/142 148/144  
  Matching-adjusted modelc 0.96 (0.85–1.10) 1.00 0.97 (0.69–1.36) 0.96 (0.67–1.36) 1.01 (0.71–1.43) 0.95 
  Fully-adjusted modeld 0.98 (0.85–1.12) 1.00 1.01 (0.71–1.44) 1.06 (0.73–1.53) 1.05 (0.73–1.52) 0.74 
 Anti–LPS-IgA, no. Ca/Co 556/556 165/152 161/133 102/127 128/144  
  Matching-adjusted modelc 0.89 (0.78–1.01) 1.00 1.09 (0.78–1.55) 0.72 (0.50–1.03) 0.78 (0.54–1.11) 0.04 
  Fully-adjusted modeld 0.86 (0.75–0.99) 1.00 1.06 (0.74–1.53) 0.67 (0.46–0.98) 0.73 (0.50–1.06) 0.02 
 Anti–LPS-IgG, no. Ca/Co 556/556 152/141 115/135 158/135 131/145  
  Matching-adjusted modelc 0.95 (0.84–1.08) 1.00 0.78 (0.55–1.10) 1.09 (0.78–1.54) 0.83 (0.58–1.19) 0.64 
  Fully-adjusted modeld 0.94 (0.82–1.08) 1.00 0.87 (0.60–1.25) 1.11 (0.78–1.59) 0.83 (0.57–1.21) 0.61 
 Total anti-Flic, no. Ca/Co 556/556 147/137 136/151 147/132 126/136  
  Matching-adjusted modelc 0.90 (0.79–1.03) 1.00 0.83 (0.59–1.16) 1.02 (0.73–1.42) 0.84 (0.58–1.21) 0.67 
  Fully-adjusted modeld 0.89 (0.77–1.03) 1.00 0.83 (0.59–1.18) 1.09 (0.77–1.54) 0.83 (0.56–1.21) 0.72 
 Total anti-LPS, no. Ca/Co 556/556 162/152 129/122 137/134 128/148  
  Matching-adjusted modelc 0.90 (0.79–1.02) 1.00 0.98 (0.69–1.38) 0.94 (0.67–1.31) 0.77 (0.53–1.10) 0.17 
  Fully-adjusted modeld 0.88 (0.76–1.01) 1.00 1.01 (0.71–1.46) 0.91 (0.64–1.30) 0.74 (0.51–1.09) 0.12 
 Total anti-Flic & LPS, no. Ca/Co 556/556 153/140 139/141 144/132 120/143  
  Matching-adjusted modelc 0.88 (0.77–1.01) 1.00 0.90 (0.65–1.24) 0.99 (0.71–1.39) 0.73 (0.50–1.05) 0.17 
  Fully-adjusted modeld 0.86 (0.75–1.00) 1.00 0.89 (0.63–1.25) 1.05 (0.74–1.49) 0.70 (0.47–1.02) 0.18 

Abbreviations: Ca/Co, case/control; Flic, flagellin; Total anti-Flic, anti–flagellin-IgA + anti–flagellin-IgG; Total anti-LPS, anti–LPS-IgA + anti–LPS-IgG; Total anti-Flic & LPS, anti–flagellin-IgA + anti–flagellin-IgG + anti–LPS-IgA + anti–LPS-IgG.

aQuartile cutoff points were based on the distribution of controls, expressed as OD readings.

bPtrend test was based on median values of each quartile.

cMatching-adjusted model based on logistic regression conditioned on matching factors (age, gender, administrative center, and date of blood collection).

dBased on matching factors plus adjustments for established confounding factors (smoking, alcohol consumption, BMI, weight circumference, physical activity, education, and total daily dietary energy consumption, fiber intake, fruits and vegetable intakes, and meat and processed meat consumption).

eQuartile cutoff points are same as those in men.

Associations of anti-LPS and anti-flagellin concentrations with colon and rectal cancer stratified by sex

In stratified analyses by anatomical subsites (Pheterogeneity = 0.64), colon cancer risk in men continued to be significantly positively associated with total anti-flagellin+LPS concentrations (fully-adjusted OR, 1.80 comparing highest vs. lowest quartiles; 95% CI, 1.04–3.10; Ptrend < 0.049; Table 3), as well as with total anti-LPS (fully-adjusted OR, 1.97; 95% CI, 1.15–3.39; Ptrend = 0.01). However, among women, higher concentrations of several biomarkers remained associated with reduced risk of colon cancer with fully-adjusted ORs of 0.59 (95% CI, 0.37–0.93), 0.57 (95% CI, 0.35–0.91), and 0.62 (95% CI, 0.39–0.98), comparing those with highest quartiles of anti–flagellin-IgA, anti–LPS-IgG, and total anti-LPS to reference, respectively (Table 3).

Table 3.

ORs (95% CI) for risk of colon cancer by quartile of baseline biomarkers of anti–LPS- and anti–flagellin-IgA and IgG: stratified by sex

ContinuousQuartilesa
Serum immunoglobulins against LPS and flagellin, OD(per 1-SD increase) OR (95% CI)Q1 ORQ2 OR (95% CI)Q3 OR (95% CI)Q4 OR (95% CI)Ptrendb
Men 
 Anti–Flic-IgA, no. Ca/Co 298/298 55/61 79/72 79/84 85/81  
  SD/cutoff point 0.74 ≤0.85 >0.85 to ≤ 1.23 >1.23 to ≤ 1.72 >1.72  
  Matching-adjusted modelc 1.01 (0.85–1.20) 1.00 1.20 (0.75–1.93) 1.03 (0.64–1.67) 1.14 (0.71–1.83) 0.73 
  Fully-adjusted modeld 0.99 (0.83–1.19) 1.00 1.29 (0.78–2.13) 1.13 (0.67–1.88) 1.09 (0.66–1.80) 0.91 
 Anti–Flic-IgG, no. Ca/Co 298/298 77/83 76/67 76/79 69/69  
  SD/cutoff point 0.79 ≤1.47 >1.47 to ≤ 2.00 >2.00 to ≤ 2.58 >2.58  
  Matching-adjusted modelc 1.00 (0.84–1.20) 1.00 1.25 (0.77–2.02) 1.06 (0.65–1.74) 1.10 (0.66–1.82) 0.90 
  Fully-adjusted modeld 1.04 (0.86–1.27) 1.00 1.37 (0.82–2.29) 1.23 (0.72–2.09) 1.25 (0.72–2.17) 0.54 
 Anti–LPS-IgA, no. Ca/Co 298/298 69/69 67/83 71/77 91/69  
  SD/cutoff point 0.72 ≤1.23 >1.23 to ≤ 1.70 >1.70 to ≤ 2.20 >2.20  
  Matching-adjusted modelc 1.19 (0.99–1.43) 1.00 0.81 (0.51–1.31) 0.95 (0.58–1.55) 1.46 (0.87–2.45) 0.12 
  Fully-adjusted modeld 1.18 (0.97–1.44) 1.00 0.85 (0.51–1.41) 0.93 (0.54–1.60) 1.44 (0.83–2.51) 0.18 
 Anti–LPS-IgG, no. Ca/Co 298/298 82/76 61/81 74/77 81/64  
  SD/cutoff point 0.60 ≤1.08 >1.08 to ≤ 1.41 >1.41 to ≤ 1.86 >1.86  
  Matching-adjusted modelc 1.14 (0.97–1.35) 1.00 0.69 (0.43–1.11) 0.88 (0.55–1.40) 1.19 (0.73–1.94) 0.33 
  Fully-adjusted modeld 1.20 (1.00–1.44) 1.00 0.77 (0.46–1.28) 0.90 (0.54–1.49) 1.34 (0.79–2.28) 0.21 
 Total anti-Flic, no. Ca/Co 298/298 69/76 75/70 71/79 83/73  
  SD/cutoff point 1.26 ≤2.50 >2.50 to ≤ 3.26 >3.26 to ≤ 4.11 >4.11  
  Matching-adjusted modelc 1.01 (0.85–1.20) 1.00 1.21 (0.75–1.95) 1.01 (0.63–1.62) 1.31 (0.80–2.15) 0.45 
  Fully-adjusted modeld 1.02 (0.84–1.22) 1.00 1.21 (0.73–2.01) 1.11 (0.67–1.85) 1.30 (0.76–2.23) 0.43 
 Total anti-LPS, no. Ca/Co 298/298 63/73 68/83 72/74 95/68  
  SD/cutoff point 1.10 ≤2.47 >2.47 to ≤ 3.10 >3.10 to ≤ 3.93 >3.93  
  Matching-adjusted modelc 1.22 (1.02–1.46) 1.00 0.97 (0.62–1.51) 1.18 (0.74–1.90) 1.83 (1.11–3.02) 0.01 
  Fully-adjusted modeld 1.25 (1.03–1.53) 1.00 1.11 (0.69–1.78) 1.26 (0.75–2.10) 1.97 (1.15–3.39) 0.01 
 Total anti-Flic & LPS, no. Ca/Co 298/298 61/76 78/80 71/70 88/72  
  SD/cutoff point 2.00 ≤5.28 >5.28 to ≤ 6.46 >6.46 to ≤ 7.91 >7.91  
  Matching-adjusted modelc 1.11 (0.94–1.33) 1.00 1.22 (0.78–1.91) 1.33 (0.83–2.13) 1.65 (1.00–2.72) 0.05 
  Fully-adjusted modeld 1.14 (0.94–1.37) 1.00 1.42 (0.88–2.31) 1.42 (0.86–2.37) 1.80 (1.04–3.10) 0.049 
Womene 
 Anti–Flic-IgA, no. Ca/Co 369/369 119/107 103/94 86/83 61/85  
  Matching-adjusted modelc 0.83 (0.70–0.97) 1.00 0.96 (0.66–1.40) 0.89 (0.59–1.32) 0.63 (0.40–0.97) 0.05 
  Fully-adjusted modeld 0.80 (0.36–0.96) 1.00 0.94 (0.63–1.39) 0.84 (0.55–1.29) 0.59 (0.37–0.93) 0.03 
 Anti–Flic-IgG, no. Ca/Co 369/369 89/84 94/100 87/88 99/97  
  Matching-adjusted modelc 0.94 (0.80–1.10) 1.00 0.89 (0.60–1.33) 0.93 (0.61–1.44) 0.96 (0.62–1.48) 0.94 
  Fully-adjusted modeld 0.95 (0.80–1.13) 1.00 0.94 (0.62–1.44) 1.07 (0.68–1.69) 1.01 (0.64–1.60) 0.84 
 Anti–LPS-IgA, no. Ca/Co 369/369 104/98 112/84 65/90 88/97  
  Matching-adjusted modelc 0.90 (0.77–1.05) 1.00 1.27 (0.83–1.94) 0.68 (0.43–1.05) 0.84 (0.55–1.29) 0.10 
  Fully-adjusted modeld 0.89 (0.75–1.04) 1.0 1.26 (0.80–1.99) 0.66 (0.42–1.06) 0.81 (0.51–1.28) 0.08 
 Anti–LPS-IgG, no. Ca/Co 369/369 117/91 78/86 91/90 83/102  
  Matching-adjusted modelc 0.85 (0.72–0.99) 1.00 0.68 (0.44–1.05) 0.76 (0.51–1.15) 0.58 (0.37–0.90) 0.03 
  Fully-adjusted modeld 0.84 (0.71–0.99) 1.00 0.74 (0.47–1.16) 0.74 (0.48–1.14) 0.57 (0.35–0.91) 0.02 
 Total anti-Flic, no. Ca/Co 369/369 96/91 104/97 91/88 78/93  
  Matching-adjusted modelc 0.85 (0.72–1.01) 1.00 1.00 (0.67–1.50) 0.97 (0.64–1.47) 0.76 (0.49–1.19) 0.26 
  Fully-adjusted modeld 0.85 (0.71–1.01) 1.00 1.08 (0.71–1.66) 1.10 (0.71–1.72) 0.75 (0.46–1.21) 0.31 
 Total anti-LPS, no. Ca/Co 369/369 116/94 81/85 89/92 83/98  
  Matching-adjusted modelc 0.85 (0.73–0.99) 1.00 0.76 (0.50–1.14) 0.77 (0.52–1.15) 0.64 (0.42–0.99) 0.06 
  Fully-adjusted modeld 0.84 (0.71–0.99) 1.00 0.74 (0.48–1.15) 0.75 (0.49–1.15) 0.62 (0.39–0.98) 0.049 
 Total anti-Flic & LPS, no. Ca/Co 369/369 109/91 93/87 87/97 80/94  
  Matching-adjusted modelc 0.83 (0.71–0.98) 1.00 0.88 (0.59–1.33) 0.73 (0.48–1.11) 0.68 (0.44–1.05) 0.05 
  Fully-adjusted modeld 0.82 (0.69–0.97) 1.00 0.90 (0.58–1.39) 0.80 (0.52–1.24) 0.66 (0.42–1.05) 0.07 
ContinuousQuartilesa
Serum immunoglobulins against LPS and flagellin, OD(per 1-SD increase) OR (95% CI)Q1 ORQ2 OR (95% CI)Q3 OR (95% CI)Q4 OR (95% CI)Ptrendb
Men 
 Anti–Flic-IgA, no. Ca/Co 298/298 55/61 79/72 79/84 85/81  
  SD/cutoff point 0.74 ≤0.85 >0.85 to ≤ 1.23 >1.23 to ≤ 1.72 >1.72  
  Matching-adjusted modelc 1.01 (0.85–1.20) 1.00 1.20 (0.75–1.93) 1.03 (0.64–1.67) 1.14 (0.71–1.83) 0.73 
  Fully-adjusted modeld 0.99 (0.83–1.19) 1.00 1.29 (0.78–2.13) 1.13 (0.67–1.88) 1.09 (0.66–1.80) 0.91 
 Anti–Flic-IgG, no. Ca/Co 298/298 77/83 76/67 76/79 69/69  
  SD/cutoff point 0.79 ≤1.47 >1.47 to ≤ 2.00 >2.00 to ≤ 2.58 >2.58  
  Matching-adjusted modelc 1.00 (0.84–1.20) 1.00 1.25 (0.77–2.02) 1.06 (0.65–1.74) 1.10 (0.66–1.82) 0.90 
  Fully-adjusted modeld 1.04 (0.86–1.27) 1.00 1.37 (0.82–2.29) 1.23 (0.72–2.09) 1.25 (0.72–2.17) 0.54 
 Anti–LPS-IgA, no. Ca/Co 298/298 69/69 67/83 71/77 91/69  
  SD/cutoff point 0.72 ≤1.23 >1.23 to ≤ 1.70 >1.70 to ≤ 2.20 >2.20  
  Matching-adjusted modelc 1.19 (0.99–1.43) 1.00 0.81 (0.51–1.31) 0.95 (0.58–1.55) 1.46 (0.87–2.45) 0.12 
  Fully-adjusted modeld 1.18 (0.97–1.44) 1.00 0.85 (0.51–1.41) 0.93 (0.54–1.60) 1.44 (0.83–2.51) 0.18 
 Anti–LPS-IgG, no. Ca/Co 298/298 82/76 61/81 74/77 81/64  
  SD/cutoff point 0.60 ≤1.08 >1.08 to ≤ 1.41 >1.41 to ≤ 1.86 >1.86  
  Matching-adjusted modelc 1.14 (0.97–1.35) 1.00 0.69 (0.43–1.11) 0.88 (0.55–1.40) 1.19 (0.73–1.94) 0.33 
  Fully-adjusted modeld 1.20 (1.00–1.44) 1.00 0.77 (0.46–1.28) 0.90 (0.54–1.49) 1.34 (0.79–2.28) 0.21 
 Total anti-Flic, no. Ca/Co 298/298 69/76 75/70 71/79 83/73  
  SD/cutoff point 1.26 ≤2.50 >2.50 to ≤ 3.26 >3.26 to ≤ 4.11 >4.11  
  Matching-adjusted modelc 1.01 (0.85–1.20) 1.00 1.21 (0.75–1.95) 1.01 (0.63–1.62) 1.31 (0.80–2.15) 0.45 
  Fully-adjusted modeld 1.02 (0.84–1.22) 1.00 1.21 (0.73–2.01) 1.11 (0.67–1.85) 1.30 (0.76–2.23) 0.43 
 Total anti-LPS, no. Ca/Co 298/298 63/73 68/83 72/74 95/68  
  SD/cutoff point 1.10 ≤2.47 >2.47 to ≤ 3.10 >3.10 to ≤ 3.93 >3.93  
  Matching-adjusted modelc 1.22 (1.02–1.46) 1.00 0.97 (0.62–1.51) 1.18 (0.74–1.90) 1.83 (1.11–3.02) 0.01 
  Fully-adjusted modeld 1.25 (1.03–1.53) 1.00 1.11 (0.69–1.78) 1.26 (0.75–2.10) 1.97 (1.15–3.39) 0.01 
 Total anti-Flic & LPS, no. Ca/Co 298/298 61/76 78/80 71/70 88/72  
  SD/cutoff point 2.00 ≤5.28 >5.28 to ≤ 6.46 >6.46 to ≤ 7.91 >7.91  
  Matching-adjusted modelc 1.11 (0.94–1.33) 1.00 1.22 (0.78–1.91) 1.33 (0.83–2.13) 1.65 (1.00–2.72) 0.05 
  Fully-adjusted modeld 1.14 (0.94–1.37) 1.00 1.42 (0.88–2.31) 1.42 (0.86–2.37) 1.80 (1.04–3.10) 0.049 
Womene 
 Anti–Flic-IgA, no. Ca/Co 369/369 119/107 103/94 86/83 61/85  
  Matching-adjusted modelc 0.83 (0.70–0.97) 1.00 0.96 (0.66–1.40) 0.89 (0.59–1.32) 0.63 (0.40–0.97) 0.05 
  Fully-adjusted modeld 0.80 (0.36–0.96) 1.00 0.94 (0.63–1.39) 0.84 (0.55–1.29) 0.59 (0.37–0.93) 0.03 
 Anti–Flic-IgG, no. Ca/Co 369/369 89/84 94/100 87/88 99/97  
  Matching-adjusted modelc 0.94 (0.80–1.10) 1.00 0.89 (0.60–1.33) 0.93 (0.61–1.44) 0.96 (0.62–1.48) 0.94 
  Fully-adjusted modeld 0.95 (0.80–1.13) 1.00 0.94 (0.62–1.44) 1.07 (0.68–1.69) 1.01 (0.64–1.60) 0.84 
 Anti–LPS-IgA, no. Ca/Co 369/369 104/98 112/84 65/90 88/97  
  Matching-adjusted modelc 0.90 (0.77–1.05) 1.00 1.27 (0.83–1.94) 0.68 (0.43–1.05) 0.84 (0.55–1.29) 0.10 
  Fully-adjusted modeld 0.89 (0.75–1.04) 1.0 1.26 (0.80–1.99) 0.66 (0.42–1.06) 0.81 (0.51–1.28) 0.08 
 Anti–LPS-IgG, no. Ca/Co 369/369 117/91 78/86 91/90 83/102  
  Matching-adjusted modelc 0.85 (0.72–0.99) 1.00 0.68 (0.44–1.05) 0.76 (0.51–1.15) 0.58 (0.37–0.90) 0.03 
  Fully-adjusted modeld 0.84 (0.71–0.99) 1.00 0.74 (0.47–1.16) 0.74 (0.48–1.14) 0.57 (0.35–0.91) 0.02 
 Total anti-Flic, no. Ca/Co 369/369 96/91 104/97 91/88 78/93  
  Matching-adjusted modelc 0.85 (0.72–1.01) 1.00 1.00 (0.67–1.50) 0.97 (0.64–1.47) 0.76 (0.49–1.19) 0.26 
  Fully-adjusted modeld 0.85 (0.71–1.01) 1.00 1.08 (0.71–1.66) 1.10 (0.71–1.72) 0.75 (0.46–1.21) 0.31 
 Total anti-LPS, no. Ca/Co 369/369 116/94 81/85 89/92 83/98  
  Matching-adjusted modelc 0.85 (0.73–0.99) 1.00 0.76 (0.50–1.14) 0.77 (0.52–1.15) 0.64 (0.42–0.99) 0.06 
  Fully-adjusted modeld 0.84 (0.71–0.99) 1.00 0.74 (0.48–1.15) 0.75 (0.49–1.15) 0.62 (0.39–0.98) 0.049 
 Total anti-Flic & LPS, no. Ca/Co 369/369 109/91 93/87 87/97 80/94  
  Matching-adjusted modelc 0.83 (0.71–0.98) 1.00 0.88 (0.59–1.33) 0.73 (0.48–1.11) 0.68 (0.44–1.05) 0.05 
  Fully-adjusted modeld 0.82 (0.69–0.97) 1.00 0.90 (0.58–1.39) 0.80 (0.52–1.24) 0.66 (0.42–1.05) 0.07 

Abbreviations: Ca/Co, case/control; Flic, flagellin; Total anti-Flic, anti–flagellin-IgA + anti–flagellin-IgG; Total anti-LPS, anti–LPS-IgA + anti–LPS-IgG; Total anti-Flic & LPS, anti–flagellin-IgA + anti–flagellin-IgG + anti–LPS-IgA + anti–LPS-IgG.

aQuartile cutoff points were based on the distribution of controls, expressed as OD readings.

bPtrend test was based on median values of each quartile.

cMatching-adjusted model based on logistic regression conditioned on matching factors (age, gender, administrative center, and date of blood collection).

dBased on matching factors plus adjustments for established confounding factors (smoking, alcohol consumption, BMI, weight circumference, physical activity, education, and total daily dietary energy consumption, fiber intake, fruits and vegetable intakes, and meat and processed meat consumption).

eQuartile cutoff points are same as those in men.

No significant association was observed between risk of rectal cancer and any of the measures for either men or women (Table 4).

Table 4.

ORs (95% CI) for risk of rectal cancer by quartile of baseline biomarkers of anti–LPS- and anti–flagellin-IgA and IgG: stratified by sex

ContinuousQuartilesa
Serum immunoglobulins against LPS and flagellin, OD(per 1-SD increase)
OR (95% CI)
Q1Q2 OR (95% CI)Q3 OR (95% CI)Q4 OR (95% CI)Ptrendb
Men 
 Anti–Flic-IgA, no. Ca/Co 211/211 38/42 39/49 72/60 62/60  
  SD/cutoff point 0.69 ≤0.75 >0.75 to ≤1.06 >1.06 to ≤1.59 > 1.59  
  Matching-adjusted modelc 1.07 (0.88–1.31) 1.00 0.87 (0.48–1.60) 1.32 (0.76–2.28) 1.17 (0.64–2.14) 0.34 
  Fully-adjusted modeld 1.08 (0.87–1.33) 1.00 0.98 (0.50–1.92) 1.34 (0.75–2.41) 1.30 (0.68–2.49) 0.28 
 Anti–Flic-IgG, no. Ca/Co 211/211 65/54 48/58 52/46 46/53  
  SD/cutoff point 0.76 ≤1.45 >1.45 to ≤1.95 >1.95 to ≤2.60 >2.60  
  Matching-adjusted modelc 0.90 (0.73–1.11) 1.00 0.70 (0.42–1.17) 0.95 (0.54–1.67) 0.72 (0.40–1.29) 0.37 
  Fully-adjusted modeld 0.95 (0.76–1.19) 1.00 0.69 (0.39–1.23) 0.94 (0.51–1.71) 0.78 (0.41–1.48) 0.57 
 Anti–LPS-IgA, no. Ca/Co 211/211 40/44 62/52 45/61 64/54  
  SD/cut off point 0.71 ≤1.14 >1.14 to ≤1.57 >1.57 to ≤2.02 >2.02  
  Matching-adjusted modelc 1.17 (0.94–1.46) 1.00 1.30 (0.71–2.37) 0.80 (0.41–1.58) 1.33 (0.68–2.62) 0.67 
  Fully-adjusted modeld 1.19 (0.93–1.53) 1.00 1.43 (0.75–2.73) 0.79 (0.38–1.66) 1.40 (0.67–2.94) 0.68 
 Anti–LPS-IgG, no. Ca/Co 211/211 53/53 61/49 44/54 53/55  
  SD/cutoff point 0.61 ≤1.01 >1.01 to ≤1.33 >1.33 to ≤1.78 >1.78  
  Matching-adjusted modelc 1.01 (0.84–1.23) 1.00 1.26 (0.73–2.15) 0.81 (0.47–1.40) 0.92 (0.52–1.63) 0.51 
  Fully-adjusted modeld 1.04 (0.85–1.28) 1.00 1.20 (0.67–2.14) 0.78 (0.43–1.39) 0.98 (0.53–1.80) 0.63 
 Total anti-Flic, no. Ca/Co 211/211 59/56 44/44 52/54 56/57  
  SD/cutoff point 1.17 ≤2.40 >2.40 to ≤3.10 >3.10 to ≤3.99 >3.99  
  Matching-adjusted modelc 0.98 (0.80–1.19) 1.00 0.95 (0.55–1.64) 0.91 (0.54–1.55) 0.93 (0.54–1.59) 0.75 
  Fully-adjusted modeld 1.01 (0.82–1.25) 1.00 0.95 (0.53–1.68) 0.92 (0.52–1.61) 1.01 (0.56–1.81) 0.99 
 Total anti-LPS, no. Ca/Co 211/211 53/42 36/61 59/58 63/50  
  SD/cutoff point 1.12 ≤2.28 >2.28 to ≤2.91 >2.91 to ≤3.75 >3.75  
  Matching-adjusted modelc 1.11 (0.89–1.37) 1.00 0.44 (0.23–0.82) 0.84 (0.46–1.54) 1.06 (0.56–2.01) 0.42 
  Fully-adjusted modeld 1.13 (0.90–1.43) 1.00 0.37 (0.19–0.72) 0.77 (0.40–1.50) 1.16 (0.58–2.32) 0.36 
 Total anti-Flic & LPS, no. Ca/Co 211/211 43/52 50/46 62/59 56/54  
  SD/cutoff point 1.98 ≤4.95 >4.95 to ≤6.11 >6.11 to ≤7.55 >7.55  
  Matching-adjusted modelc 1.04 (0.85–1.28) 1.00 1.34 (0.75–2.41) 1.29 (0.75–2.21) 1.30 (0.71–2.39) 0.44 
  Fully-adjusted modeld 1.08 (0.86–1.35) 1.00 1.27 (0.68–2.39) 1.24 (0.69–2.21) 1.49 (0.77–2.90) 0.29 
Womene 
 Anti–Flic-IgA, no. Ca/Co 187/187 61/58 43/50 41/40 42/39  
  Matching-adjusted modelc 0.99 (0.77–1.28) 1.00 0.84 (0.49–1.43) 0.98 (0.52–1.87) 1.04 (0.54–2.00) 0.83 
  Fully-adjusted modeld 0.96 (0.73–1.26) 1.00 0.72 (0.41–1.26) 0.82 (0.41–1.64) 0.95 (0.47–1.92) 0.92 
 Anti–Flic-IgG, no. Ca/Co 187/187 43/46 46/41 49/54 49/46  
  Matching-adjusted modelc 1.01 (0.82–1.26) 1.00 1.18 (0.65–2.16) 0.98 (0.54–1.79) 1.13 (0.62–2.07) 0.84 
  Fully-adjusted modeld 1.01 (0.80–1.28) 1.00 1.17 (0.62–2.20) 1.06 (0.55–2.02) 1.15 (0.60–2.21) 0.77 
 Anti–LPS-IgA, no. Ca/Co 187/187 53/56 58/47 37/40 39/44  
  Matching-adjusted modelc 0.86 (0.68–1.10) 1.00 1.32 (0.73–2.40) 0.98 (0.53–1.81) 0.92 (0.46–1.83) 0.58 
  Fully-adjusted modeld 0.82 (0.63–1.07) 1.00 1.34 (0.71–2.52) 0.89 (0.47–1.71) 0.84 (0.40–1.74) 0.39 
 Anti–LPS-IgG, no. Ca/Co 187/187 39/47 39/50 61/46 48/44  
  Matching-adjusted modelc 1.21 (0.96–1.53) 1.00 0.94 (0.52–1.72) 1.94 (1.01–3.73) 1.60 (0.83–3.09) 0.10 
  Fully-adjusted modeld 1.22 (0.96–1.56) 1.00 1.11 (0.59–2.10) 2.21 (1.11–4.40) 1.74 (0.87–3.50) 0.07 
 Total anti-Flic, no. Ca/Co 187/187 49/44 38/55 57/46 43/42  
  Matching-adjusted modelc 1.01 (0.80–1.27) 1.00 0.64 (0.36–1.14) 1.10 (0.62–1.96) 0.93 (0.50–1.75) 0.69 
  Fully-adjusted modeld 0.99 (0.77–1.27) 1.00 0.61 (0.33–1.11) 1.10 (0.59–2.03) 0.92 (0.47–1.80) 0.73 
 Total anti-LPS, no. Ca/Co 187/187 49/58 43/38 49/41 46/50  
  Matching-adjusted modelc 1.03 (0.81–1.32) 1.00 1.38 (0.75–2.54) 1.48 (0.80–2.75) 1.15 (0.61–2.17) 0.64 
  Fully-adjusted modeld 1.00 (0.77–1.31) 1.00 1.49 (0.78–2.85) 1.44 (0.77–2.72) 1.13 (0.58–2.23) 0.73 
 Total anti-Flic & LPS, no. Ca/Co 187/187 49/48 45/53 50/42 43/44  
  Matching-adjusted modelc 1.02 (0.80–1.31) 1.00 0.84 (0.48–1.48) 1.16 (0.64–2.08) 0.95 (0.50–1.79) 0.80 
  Fully-adjusted modeld 1.00 (0.76–1.30) 1.00 0.80 (0.44–1.46) 1.17 (0.63–2.19) 0.91 (0.46–1.78) 0.85 
ContinuousQuartilesa
Serum immunoglobulins against LPS and flagellin, OD(per 1-SD increase)
OR (95% CI)
Q1Q2 OR (95% CI)Q3 OR (95% CI)Q4 OR (95% CI)Ptrendb
Men 
 Anti–Flic-IgA, no. Ca/Co 211/211 38/42 39/49 72/60 62/60  
  SD/cutoff point 0.69 ≤0.75 >0.75 to ≤1.06 >1.06 to ≤1.59 > 1.59  
  Matching-adjusted modelc 1.07 (0.88–1.31) 1.00 0.87 (0.48–1.60) 1.32 (0.76–2.28) 1.17 (0.64–2.14) 0.34 
  Fully-adjusted modeld 1.08 (0.87–1.33) 1.00 0.98 (0.50–1.92) 1.34 (0.75–2.41) 1.30 (0.68–2.49) 0.28 
 Anti–Flic-IgG, no. Ca/Co 211/211 65/54 48/58 52/46 46/53  
  SD/cutoff point 0.76 ≤1.45 >1.45 to ≤1.95 >1.95 to ≤2.60 >2.60  
  Matching-adjusted modelc 0.90 (0.73–1.11) 1.00 0.70 (0.42–1.17) 0.95 (0.54–1.67) 0.72 (0.40–1.29) 0.37 
  Fully-adjusted modeld 0.95 (0.76–1.19) 1.00 0.69 (0.39–1.23) 0.94 (0.51–1.71) 0.78 (0.41–1.48) 0.57 
 Anti–LPS-IgA, no. Ca/Co 211/211 40/44 62/52 45/61 64/54  
  SD/cut off point 0.71 ≤1.14 >1.14 to ≤1.57 >1.57 to ≤2.02 >2.02  
  Matching-adjusted modelc 1.17 (0.94–1.46) 1.00 1.30 (0.71–2.37) 0.80 (0.41–1.58) 1.33 (0.68–2.62) 0.67 
  Fully-adjusted modeld 1.19 (0.93–1.53) 1.00 1.43 (0.75–2.73) 0.79 (0.38–1.66) 1.40 (0.67–2.94) 0.68 
 Anti–LPS-IgG, no. Ca/Co 211/211 53/53 61/49 44/54 53/55  
  SD/cutoff point 0.61 ≤1.01 >1.01 to ≤1.33 >1.33 to ≤1.78 >1.78  
  Matching-adjusted modelc 1.01 (0.84–1.23) 1.00 1.26 (0.73–2.15) 0.81 (0.47–1.40) 0.92 (0.52–1.63) 0.51 
  Fully-adjusted modeld 1.04 (0.85–1.28) 1.00 1.20 (0.67–2.14) 0.78 (0.43–1.39) 0.98 (0.53–1.80) 0.63 
 Total anti-Flic, no. Ca/Co 211/211 59/56 44/44 52/54 56/57  
  SD/cutoff point 1.17 ≤2.40 >2.40 to ≤3.10 >3.10 to ≤3.99 >3.99  
  Matching-adjusted modelc 0.98 (0.80–1.19) 1.00 0.95 (0.55–1.64) 0.91 (0.54–1.55) 0.93 (0.54–1.59) 0.75 
  Fully-adjusted modeld 1.01 (0.82–1.25) 1.00 0.95 (0.53–1.68) 0.92 (0.52–1.61) 1.01 (0.56–1.81) 0.99 
 Total anti-LPS, no. Ca/Co 211/211 53/42 36/61 59/58 63/50  
  SD/cutoff point 1.12 ≤2.28 >2.28 to ≤2.91 >2.91 to ≤3.75 >3.75  
  Matching-adjusted modelc 1.11 (0.89–1.37) 1.00 0.44 (0.23–0.82) 0.84 (0.46–1.54) 1.06 (0.56–2.01) 0.42 
  Fully-adjusted modeld 1.13 (0.90–1.43) 1.00 0.37 (0.19–0.72) 0.77 (0.40–1.50) 1.16 (0.58–2.32) 0.36 
 Total anti-Flic & LPS, no. Ca/Co 211/211 43/52 50/46 62/59 56/54  
  SD/cutoff point 1.98 ≤4.95 >4.95 to ≤6.11 >6.11 to ≤7.55 >7.55  
  Matching-adjusted modelc 1.04 (0.85–1.28) 1.00 1.34 (0.75–2.41) 1.29 (0.75–2.21) 1.30 (0.71–2.39) 0.44 
  Fully-adjusted modeld 1.08 (0.86–1.35) 1.00 1.27 (0.68–2.39) 1.24 (0.69–2.21) 1.49 (0.77–2.90) 0.29 
Womene 
 Anti–Flic-IgA, no. Ca/Co 187/187 61/58 43/50 41/40 42/39  
  Matching-adjusted modelc 0.99 (0.77–1.28) 1.00 0.84 (0.49–1.43) 0.98 (0.52–1.87) 1.04 (0.54–2.00) 0.83 
  Fully-adjusted modeld 0.96 (0.73–1.26) 1.00 0.72 (0.41–1.26) 0.82 (0.41–1.64) 0.95 (0.47–1.92) 0.92 
 Anti–Flic-IgG, no. Ca/Co 187/187 43/46 46/41 49/54 49/46  
  Matching-adjusted modelc 1.01 (0.82–1.26) 1.00 1.18 (0.65–2.16) 0.98 (0.54–1.79) 1.13 (0.62–2.07) 0.84 
  Fully-adjusted modeld 1.01 (0.80–1.28) 1.00 1.17 (0.62–2.20) 1.06 (0.55–2.02) 1.15 (0.60–2.21) 0.77 
 Anti–LPS-IgA, no. Ca/Co 187/187 53/56 58/47 37/40 39/44  
  Matching-adjusted modelc 0.86 (0.68–1.10) 1.00 1.32 (0.73–2.40) 0.98 (0.53–1.81) 0.92 (0.46–1.83) 0.58 
  Fully-adjusted modeld 0.82 (0.63–1.07) 1.00 1.34 (0.71–2.52) 0.89 (0.47–1.71) 0.84 (0.40–1.74) 0.39 
 Anti–LPS-IgG, no. Ca/Co 187/187 39/47 39/50 61/46 48/44  
  Matching-adjusted modelc 1.21 (0.96–1.53) 1.00 0.94 (0.52–1.72) 1.94 (1.01–3.73) 1.60 (0.83–3.09) 0.10 
  Fully-adjusted modeld 1.22 (0.96–1.56) 1.00 1.11 (0.59–2.10) 2.21 (1.11–4.40) 1.74 (0.87–3.50) 0.07 
 Total anti-Flic, no. Ca/Co 187/187 49/44 38/55 57/46 43/42  
  Matching-adjusted modelc 1.01 (0.80–1.27) 1.00 0.64 (0.36–1.14) 1.10 (0.62–1.96) 0.93 (0.50–1.75) 0.69 
  Fully-adjusted modeld 0.99 (0.77–1.27) 1.00 0.61 (0.33–1.11) 1.10 (0.59–2.03) 0.92 (0.47–1.80) 0.73 
 Total anti-LPS, no. Ca/Co 187/187 49/58 43/38 49/41 46/50  
  Matching-adjusted modelc 1.03 (0.81–1.32) 1.00 1.38 (0.75–2.54) 1.48 (0.80–2.75) 1.15 (0.61–2.17) 0.64 
  Fully-adjusted modeld 1.00 (0.77–1.31) 1.00 1.49 (0.78–2.85) 1.44 (0.77–2.72) 1.13 (0.58–2.23) 0.73 
 Total anti-Flic & LPS, no. Ca/Co 187/187 49/48 45/53 50/42 43/44  
  Matching-adjusted modelc 1.02 (0.80–1.31) 1.00 0.84 (0.48–1.48) 1.16 (0.64–2.08) 0.95 (0.50–1.79) 0.80 
  Fully-adjusted modeld 1.00 (0.76–1.30) 1.00 0.80 (0.44–1.46) 1.17 (0.63–2.19) 0.91 (0.46–1.78) 0.85 

Abbreviations: Ca/Co, case/control; Flic, flagellin; Total anti-Flic, anti–flagellin-IgA + anti–flagellin-IgG; Total anti-LPS, anti–LPS-IgA + anti–LPS-IgG; Total anti-Flic & LPS, anti–flagellin-IgA + anti–flagellin-IgG + anti–LPS-IgA + anti–LPS-IgG.

aQuartile cutoff points were based on the distribution of controls, expressed as OD readings.

bPtrend test was based on median values of each quartile.

cMatching-adjusted model based on logistic regression conditioned on matching factors (age, gender, administrative center, and date of blood collection).

dBased on matching factors plus adjustments for established confounding factors (smoking, alcohol consumption, BMI, weight circumference, physical activity, education, and total daily dietary energy consumption, fiber intake, fruits and vegetable intakes, and meat and processed meat consumption).

eQuartile cutoff points are same as those in men.

Interactions with inflammation, body size, and dietary fat

The analysis of the interaction between total anti-LPS+flagellin level and inflammation (hs-CRP), body size (waist circumference and BMI), dietary fat intake, and alcohol consumption showed that, among men, the positive association between colorectal cancer risk and total anti-LPS+flagellin level was stronger at higher levels of hs-CRP (OR, 2.35 comparing highest hs-CRP and highest tertile of total anti-LPS+flagellin vs. lowest hs-CRP and lowest tertile of total anti-LPS+flagellin; 95% CI, 1.45–3.81; Pinteraction = 0.002), waist circumference (OR, 1.97; 95% CI, 1.24–3.13; Pinteraction = 0.01), BMI (OR, 1.77; 95% CI, 1.13–2.78; Pinteraction = 0.03), and alcohol (OR, 1.71; 95% CI, 1.09–2.69; Pinteraction = 0.02). No interaction was observed in any of these factors among women (Pinteraction > 0.05 for all; Supplementary Table S2).

Sensitivity analysis

After excluding cases that occurred during the first 2 years of follow-up and their matched controls to avoid possible reverse causality, the findings did not change substantially for any of the serologic biomarkers in both colon and rectal cancers for either sex (Supplementary Table S3). Similar results were observed after excluding participants in the countries with lowest (Denmark) and highest (Greece) anti–LPS- and anti-flagellin biomarker exposure levels (data not shown). Spline models showed that the associations between anti-flagellin and anti-LPS biomarkers and risk of colon or rectal cancers were linear (data not shown).

In this nested case–control study, we investigated the associations of serologic bacterial markers of anti–LPS- and anti–flagellin-IgA and IgG with colorectal cancer risk. No significant associations were observed with colorectal cancer risk, but subgroup analyses by sex revealed a positive association in men for anti-LPS and anti-flagellin markers combined, whereas in women, the associations were inverse.

One key mechanism whereby microbiota may influence colorectal cancer development is through intestinal barrier dysfunction (29). There is an emerging recognition of the ability of the GI tract to regulate the trafficking of macromolecules between the environment and the host through a barrier mechanism (30). A growing body of evidence supports a link between increased intestinal permeability and several GI disorders such as inflammatory bowel disease (IBD) (31), which is a known risk factor for colorectal cancer. It has been suggested that some dietary/lifestyle exposures (e.g., total fat intake, body weight) and physiologic factors (e.g., inflammation) may exacerbate intestinal permeability, leading to increased exposure of the colonic epithelium to endotoxins and greater leakage of endotoxins into the systemic circulation (32, 33).

The impact of bacteria on the development of colorectal cancer has been mostly studied from the perspective of inflammatory responses. It has become clear that the microbiota has a major influence on immune responses, and chronic inflammation is a well-established risk factor for colorectal cancer (34). LPS has been suggested to be involved in colorectal cancer development through their roles in stimulating the immune system by binding cell-surface Toll-like receptor (TLR)-4, the predominant receptor for LPS, and activating transcription factors, such as NF-κB, resulting in an increased production of proinflammatory cytokines, such as TNFα, IL1, and IL6 (35). Flagellin is recognized by both TLR-5 and the NLRC4 inflammasome, which elicits immune signals by activation of NF-κB and caspase-1, respectively, and hence promotes systemic inflammation by production of multiple inflammatory cytokines (36, 37).

Despite a growing body of evidence from in vitro and in vivo studies on the role of the microbiome in the development of colorectal cancer, limited epidemiologic studies have thus far been available to show associations between bacterial endotoxin exposure and colorectal adenomas or colorectal cancer. Two recent studies have observed a positive relationship between endotoxin and colorectal adenomas (38, 39) with the strongest associations observed for dysplastic lesions (39). Our results showing a positive association of serum LPS and flagellin biomarkers and colorectal cancer in men are in line with the results of these studies on the role of bacteria exposure in colorectal cancer carcinogenesis. However, these studies did not report sex-stratified findings so do not permit compassion with our findings in women.

We observed in hypothesis-generating analyses that the positive associations between anti-LPS and anti-flagellin levels and risk of colorectal cancer in men were stronger in higher levels of hs-CRP, waist circumference, BMI, and alcohol intake, results which, if replicated, suggest that these factors may play a role in exacerbating the colorectal cancer–promotive effects of LPS and flagellin. Also worthy of examination is the possibility that body size, inflammation, and alcohol intake may influence intestinal permeability and so leads to increased exposure to bacterial products.

Based on the observations from the above-mentioned studies, an inverse association between anti-LPS and anti-flagellin levels and colorectal cancer risk that we observed among women was unexpected. However, other studies have previously demonstrated inverse associations between environmental endotoxin exposures and the risk of lung and other cancers in occupational settings. Protective effects of environmental/occupational endotoxin exposure on lung cancer have been consistently demonstrated in studies of cotton textile due to raw cotton fiber or dust being contaminated with bacterial endotoxin (40–42) and farming industries (43). Differences between men and women have also been observed among cotton plant workers where there was an increased risk of colon and liver cancers in men while women had lower risk of rectal/anal and liver cancers (44). However, these previous studies were based on occupational cohorts with high endotoxin exposures, whereas the endotoxin measures of our study subjects are likely to be derived largely from the colonic bacteria rather than the environment. Therefore, careful interpretation is required when comparing our findings with those of the previous studies looking at specific subject groups.

Two mechanisms may be involved in the differences between men and women that we observed in the associations between endotoxin and risk of colorectal cancer. First, complex interactions between the innate and adaptive immune systems are important underlying mechanisms of associations between endotoxin and carcinogenesis (45). The differences between men and women are observed and could therefore result from well-established sex-based differences in the immune systems that result in women having a more vigorous immune response, both cellular and humoral, than men (46–48). Second, it is possible that the composition of microbiota could differ in men and women as sex differences have been observed in the composition of skin microbiota (49). It is therefore possible that different organisms might have different associations with colon carcinogenesis and so account for the differences between men and women we observed. Such possibilities have yet to be studied in detail.

Lastly, it is possible that our gender-specific observations are due to chance, despite the relatively large size of the present study. Therefore, replication of these findings and deeper exploration of the sex-specific bacterial exposure and colorectal cancer hypothesis is required.

The present study has several strengths. Our study is the largest prospective cohort so far to investigate bacterial exposures and colorectal cancer risk. Therefore, we had a large enough sample size to be able to stratify by anatomical subsites of colorectal cancer and by gender. To our knowledge, no previous studies on bacterial exposure and colorectal cancer risk have had a sufficiently large enough sample size to conduct stratified analyses.

We also have several limitations in the study. First, because the gut is colonized by complex bacterial communities, elevated anti-LPS or anti-flagellin levels alone may not be sufficient to promote inflammation and tumor progression (38). Another limitation is that we measured the anti-LPS and anti-flagellin concentrations in serum, not in the colonic mucosa, which could be more relevant for colorectal carcinoma formation. Indeed, the assay we applied measures serum immunoreactivity to common bacterial flagellin monomers, which have highly conserved regions common to many flagellins in the microbiota. Although differences in such flagellin immunoreactivity have been thought to reflect differences in gut permeability, they may also arise from differences in microbiota composition and/or gene expression. Thus, better clarification of the source and biologic properties of these compounds is a task for future research. Moreover, biosamples were available only from the time of recruitment into the cohort, and thus we only had a single-blood measure taken at one point in time.

In summary, we found no overall association between bacterial exposure levels, measured by anti–LPS- and anti-flagellin-IgA and IgG, and risk of colorectal cancer. However, in subgroup analysis by sex, we found some biomarker levels to be positively associated with colorectal cancer risk among men, whereas they were inversely associated with colorectal cancer risk among women. Further studies are warranted to elucidate the underlying mechanisms of bacterial exposure and colorectal cancer by sex as well as the sex-specific role of inflammation and immune response on colorectal cancer risk.

No potential conflicts of interest were disclosed.

The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the article.

Conception and design: G. McKeown-Eyssen, A. Tjønneland, K. Overvad, V. Krogh, R. Tumino, H.B. Bueno-de-Mesquita, P.H. Peeters, E. Weiderpass, M.-J. Sánchez, A. Barricarte, M. Dorronsoro, K.-T. Khaw, H. Freisling, W.R. Bruce, M. Jenab

Development of methodology: G. McKeown-Eyssen, R. Tumino, H.B. Bueno-de-Mesquita, E. Weiderpass, A. Barricarte, M. Dorronsoro, M. Jenab

Acquisition of data (provided animals, acquired and managed patients, provided facilities, etc.): H.Q. Tran, A.T. Gewirtz, K. Overvad, M.-C. Boutron-Ruault, T. Kühn, R. Kaaks, H. Boeing, A. Trichopoulou, E. Vasilopoulou, D. Palli, V. Krogh, A. Mattiello, R. Tumino, A. Naccarati, H.B. Bueno-de-Mesquita, P.H. Peeters, E. Weiderpass, J.R. Quirós, N. Sala, M.-J. Sánchez, A. Barricarte, M. Dorronsoro, M. Werner, N.J. Wareham, K.-T. Khaw, K.E. Bradbury, M. Jenab

Analysis and interpretation of data (e.g., statistical analysis, biostatistics, computational analysis): S.Y. Kong, H.Q. Tran, A.T. Gewirtz, G. McKeown-Eyssen, V. Fedirko, A. Tjønneland, D. Palli, E. Weiderpass, M. Werner, N.J. Wareham, H. Freisling, P. Ferrari, M.J. Gunter, M. Jenab

Writing, review, and/or revision of the manuscript: S.Y. Kong, G. McKeown-Eyssen, V. Fedirko, I. Romieu, A. Tjønneland, A. Olsen, K. Overvad, M.-C. Boutron-Ruault, N. Bastide, A. Affret, T. Kühn, R. Kaaks, H. Boeing, K. Aleksandrova, A. Trichopoulou, M. Kritikou, E. Vasilopoulou, D. Palli, V. Krogh, A. Mattiello, A. Naccarati, H.B. Bueno-de-Mesquita, P.H. Peeters, E. Weiderpass, J.R. Quirós, N. Sala, M.-J. Sánchez, J.M. Huerta Castaño, A. Barricarte, M. Dorronsoro, M. Werner, N.J. Wareham, K.-T. Khaw, K.E. Bradbury, H. Freisling, F. Stavropoulou, P. Ferrari, M.J. Gunter, A.J. Cross, E. Riboli, W.R. Bruce, M. Jenab

Administrative, technical, or material support (i.e., reporting or organizing data, constructing databases): S.Y. Kong, T. Kühn, H. Boeing, M. Kritikou, R. Tumino, E. Weiderpass, J.R. Quirós, K.-T. Khaw, K.E. Bradbury, M. Jenab

Study supervision: M. Kritikou, R. Tumino, H.B. Bueno-de-Mesquita, P.H. Peeters, E. Weiderpass, M. Dorronsoro, M. Jenab

Other (co-author on the grant which supported the research): G. McKeown-Eyssen

This work was funded by Wereld Kanker Onderzoek Fonds as part of the World Cancer Research Fund (WCRF) International Regular Grant Programme (grant number 2010–251; pricipal investigator: M. Jenab). The coordination of EPIC is financially supported by the European Commission (DG-SANCO) and the International Agency for Research on Cancer. The national cohorts are supported by Danish Cancer Society (Denmark); Ligue Contre le Cancer, Institut Gustave Roussy, Mutuelle Générale de l'Education Nationale, Institut National de la Santé et de la Recherche Médicale (INSERM, France); Deutsche Krebshilfe, Deutsches Krebsforschungszentrum and Federal Ministry of Education and Research (Germany); the Hellenic Health Foundation (Greece); Associazione Italiana per la Ricerca sul Cancro-AIRC-Italy and National Research Council (Italy); Dutch Ministry of Public Health, Welfare, and Sports (VWS), Netherlands Cancer Registry (NKR), LK Research Funds, Dutch Prevention Funds, Dutch ZON (Zorg Onderzoek Nederland), World Cancer Research Fund (WCRF), Statistics Netherlands (the Netherlands); Nordic Centre of Excellence programme on Food, Nutrition, and Health (Norway); Health Research Fund (FIS), PI13/00061 to Granada, Regional Governments of Andalucía, Asturias, Basque Country, Murcia (no. 6236) and Navarra, ISCIII RETIC (RD06/0020; Spain); Swedish Cancer Society, Swedish Scientific Council and County Councils of Skåne and Västerbotten (Sweden); Cancer Research UK (14136 to EPIC-Norfolk; C570/A16491 to EPIC-Oxford; K.E. Bradbury and K.-T. Khaw), Medical Research Council (1000143 to EPIC-Norfolk, United Kingdom).

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

1.
Jemal
A
,
Bray
F
,
Center
MM
,
Ferlay
J
,
Ward
E
,
Forman
D
. 
Global cancer statistics
.
CA Cancer J Clin
2011
;
61
:
69
90
.
2.
Cani
PD
,
Delzenne
NM
. 
Gut microflora as a target for energy and metabolic homeostasis
.
Curr Opin Clin Nutr Metab Care
2007
;
10
:
729
34
.
3.
Ashida
H
,
Ogawa
M
,
Kim
M
,
Mimuro
H
,
Sasakawa
C
. 
Bacteria and host interactions in the gut epithelial barrier
.
Nat Chem Biol
2012
;
8
:
36
45
.
4.
Ahn
J
,
Sinha
R
,
Pei
Z
,
Dominianni
C
,
Wu
J
,
Shi
J
, et al
Human gut microbiome and risk for colorectal cancer
.
J Natl Cancer Inst
2013
;
105
:
1907
11
.
5.
Pussinen
PJ
,
Havulinna
AS
,
Lehto
M
,
Sundvall
J
,
Salomaa
V
. 
Endotoxemia is associated with an increased risk of incident diabetes
.
Diabetes Care
2011
;
34
:
392
7
.
6.
Sanders
CJ
,
Yu
Y
,
Moore
DA
 3rd
,
Williams
IR
,
Gewirtz
AT
. 
Humoral immune response to flagellin requires T cells and activation of innate immunity
.
J Immunol
2006
;
177
:
2810
8
.
7.
Amar
J
,
Burcelin
R
,
Ruidavets
JB
,
Cani
PD
,
Fauvel
J
,
Alessi
MC
, et al
Energy intake is associated with endotoxemia in apparently healthy men
.
Am J Clin Nutr
2008
;
87
:
1219
23
.
8.
Atreya
R
,
Neurath
MF
. 
Signaling molecules: the pathogenic role of the IL-6/STAT-3 trans signaling pathway in intestinal inflammation and in colonic cancer
.
Curr Drug Targets
2008
;
9
:
369
74
.
9.
Nimptsch
K
,
Aleksandrova
K
,
Boeing
H
,
Janke
J
,
Lee
YA
,
Jenab
M
, et al
Association of CRP genetic variants with blood concentrations of C-reactive protein and colorectal cancer risk
.
Int J Cancer
2015
;
136
:
1181
92
.
10.
Ziegler
TR
,
Luo
M
,
Estivariz
CF
,
Moore
DA
 3rd
,
Sitaraman
SV
,
Hao
L
, et al
Detectable serum flagellin and lipopolysaccharide and upregulated anti-flagellin and lipopolysaccharide immunoglobulins in human short bowel syndrome
.
Am J Physiol Regul Integr Comp Physiol
2008
;
294
:
R402
10
.
11.
Sitaraman
SV
,
Klapproth
JM
,
Moore
DA
 3rd
,
Landers
C
,
Targan
S
,
Williams
IR
, et al
Elevated flagellin-specific immunoglobulins in Crohn's disease
.
Am J Physiol Gastrointest Liver Physiol
2005
;
288
:
G403
6
.
12.
Lodes
MJ
,
Cong
Y
,
Elson
CO
,
Mohamath
R
,
Landers
CJ
,
Targan
SR
, et al
Bacterial flagellin is a dominant antigen in Crohn disease
.
J Clin Invest
2004
;
113
:
1296
306
.
13.
Grivennikov
SI
,
Wang
K
,
Mucida
D
,
Stewart
CA
,
Schnabl
B
,
Jauch
D
, et al
Adenoma-linked barrier defects and microbial products drive IL-23/IL-17-mediated tumour growth
.
Nature
2012
;
491
:
254
8
.
14.
Hope
ME
,
Hold
GL
,
Kain
R
,
El-Omar
EM
. 
Sporadic colorectal cancer–role of the commensal microbiota
.
FEMS Microbiol Lett
2005
;
244
:
1
7
.
15.
Riboli
E
,
Kaaks
R
. 
The EPIC Project: rationale and study design. European Prospective Investigation into Cancer and Nutrition
.
Int J Epidemiol
1997
;
26
:
S6
14
.
16.
Riboli
E
,
Hunt
KJ
,
Slimani
N
,
Ferrari
P
,
Norat
T
,
Fahey
M
, et al
European Prospective Investigation into Cancer and Nutrition (EPIC): study populations and data collection
.
Public Health Nutr
2002
;
5
:
1113
24
.
17.
Gewirtz
AT
,
Vijay-Kumar
M
,
Brant
SR
,
Duerr
RH
,
Nicolae
DL
,
Cho
JH
. 
Dominant-negative TLR5 polymorphism reduces adaptive immune response to flagellin and negatively associates with Crohn's disease
.
Am J Physiol Gastrointest Liver Physiol
2006
;
290
:
G1157
63
.
18.
Rinaldi
S
,
Rohrmann
S
,
Jenab
M
,
Biessy
C
,
Sieri
S
,
Palli
D
, et al
Glycosylated hemoglobin and risk of colorectal cancer in men and women, the European prospective investigation into cancer and nutrition
.
Cancer Epidemiol Biomarkers Prev
2008
;
17
:
3108
15
.
19.
Aleksandrova
K
,
Jenab
M
,
Boeing
H
,
Jansen
E
,
Bueno-de-Mesquita
HB
,
Rinaldi
S
, et al
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
2010
;
172
:
407
18
.
20.
Aleksandrova
K
,
Boeing
H
,
Jenab
M
, Bas
Bueno-de-Mesquita
H
,
Jansen
E
,
van Duijnhoven
FJ
, et al
Metabolic syndrome and risks of colon and rectal cancer: the European prospective investigation into cancer and nutrition study
.
Cancer Prev Res
2011
;
4
:
1873
83
.
21.
Murphy
N
,
Norat
T
,
Ferrari
P
,
Jenab
M
,
Bueno-de-Mesquita
B
,
Skeie
G
, et al
Dietary fibre intake and risks of cancers of the colon and rectum in the European prospective investigation into cancer and nutrition (EPIC)
.
PLoS One
2012
;
7
:
e39361
.
22.
van Duijnhoven
FJ
,
Bueno-De-Mesquita
HB
,
Ferrari
P
,
Jenab
M
,
Boshuizen
HC
,
Ros
MM
, et al
Fruit, vegetables, and colorectal cancer risk: the European Prospective Investigation into Cancer and Nutrition
.
Am J Clin Nutr
2009
;
89
:
1441
52
.
23.
Norat
T
,
Bingham
S
,
Ferrari
P
,
Slimani
N
,
Jenab
M
,
Mazuir
M
, et al
Meat, fish, and colorectal cancer risk: the European Prospective Investigation into cancer and nutrition
.
J Natl Cancer Inst
2005
;
97
:
906
16
.
24.
Leufkens
AM
,
Van Duijnhoven
FJ
,
Siersema
PD
,
Boshuizen
HC
,
Vrieling
A
,
Agudo
A
, et al
Cigarette smoking and colorectal cancer risk in the European Prospective Investigation into Cancer and Nutrition Study
.
Clin Gastroenterol Hepatol
2011
;
9
:
137
44
.
25.
Steins Bisschop
CN
,
van Gils
CH
,
Emaus
MJ
,
Bueno-de-Mesquita
HB
,
Monninkhof
EM
,
Boeing
H
, et al
Weight change later in life and colon and rectal cancer risk in participants in the EPIC-PANACEA study1,3
.
Am J Clin Nutr
2014
;
99
:
139
47
.
26.
Ferrari
P
,
Jenab
M
,
Norat
T
,
Moskal
A
,
Slimani
N
,
Olsen
A
, et al
Lifetime and baseline alcohol intake and risk of colon and rectal cancers in the European Prospective Investigation into Cancer And Nutrition (EPIC)
.
Int J Cancer
2007
;
121
:
2065
72
.
27.
Cani
PD
,
Bibiloni
R
,
Knauf
C
,
Waget
A
,
Neyrinck
AM
,
Delzenne
NM
, et al
Changes in gut microbiota control metabolic endotoxemia-induced inflammation in high-fat diet-induced obesity and diabetes in mice
.
Diabetes
2008
;
57
:
1470
81
.
28.
Li
R
,
Hertzmark
E
,
Louie
M
,
Chen
L
,
Spiegelman
D
. 
The SAS LGTPHCURV9 Macro
. 
2010
.
29.
Schwabe
RF
,
Jobin
C
. 
The microbiome and cancer
.
Nat Rev Cancer
2013
;
13
:
800
12
.
30.
Fasano
A
. 
Zonulin and its regulation of intestinal barrier function: the biological door to inflammation, autoimmunity, and cancer
.
Physiol Rev
2011
;
91
:
151
75
.
31.
Shen
L
,
Su
L
,
Turner
JR
. 
Mechanisms and functional implications of intestinal barrier defects
.
Dig Dis
2009
;
27
:
443
9
.
32.
Teixeira
TF
,
Collado
MC
,
Ferreira
CL
,
Bressan
J
,
Peluzio
Mdo C
. 
Potential mechanisms for the emerging link between obesity and increased intestinal permeability
.
Nutr Res
2012
;
32
:
637
47
.
33.
Burcelin
R
,
Serino
M
,
Chabo
C
,
Blasco-Baque
V
,
Amar
J
. 
Gut microbiota and diabetes: from pathogenesis to therapeutic perspective
.
Acta Diabetol
2011
;
48
:
257
73
.
34.
Louis
P
,
Hold
GL
,
Flint
HJ
. 
The gut microbiota, bacterial metabolites and colorectal cancer
.
Nat Rev Microbiol
2014
;
12
:
661
72
.
35.
Killeen
SD
,
Wang
JH
,
Andrews
EJ
,
Redmond
HP
. 
Bacterial endotoxin enhances colorectal cancer cell adhesion and invasion through TLR-4 and NF-kappaB-dependent activation of the urokinase plasminogen activator system
.
Br J Cancer
2009
;
100
:
1589
602
.
36.
Hayashi
F
,
Smith
KD
,
Ozinsky
A
,
Hawn
TR
,
Yi
EC
,
Goodlett
DR
, et al
The innate immune response to bacterial flagellin is mediated by Toll-like receptor 5
.
Nature
2001
;
410
:
1099
103
.
37.
Steiner
TS
. 
How flagellin and toll-like receptor 5 contribute to enteric infection
.
Infect Immun
2007
;
75
:
545
52
.
38.
Kang
M
,
Edmundson
P
,
Araujo-Perez
F
,
McCoy
AN
,
Galanko
J
,
Keku
TO
. 
Association of plasma endotoxin, inflammatory cytokines and risk of colorectal adenomas
.
BMC Cancer
2013
;
13
:
91
.
39.
Lee
KK
,
Yum
KS
. 
Association of endotoxins and colon polyp: a case-control study
.
J Korean Med Sci
2012
;
27
:
1062
5
.
40.
Wernli
KJ
,
Ray
RM
,
Gao
DL
,
Thomas
DB
,
Checkoway
H
. 
Cancer among women textile workers in Shanghai, China: overall incidence patterns, 1989–1998
.
Am J Ind Med
2003
;
44
:
595
9
.
41.
Hodgson
JT
,
Jones
RD
. 
Mortality of workers in the British cotton industry in 1968–1984
.
Scand J Work Environ Health
1990
;
16
:
113
20
.
42.
Astrakianakis
G
,
Seixas
NS
,
Ray
R
,
Camp
JE
,
Gao
DL
,
Feng
Z
, et al
Lung cancer risk among female textile workers exposed to endotoxin
.
J Natl Cancer Inst
2007
;
99
:
357
64
.
43.
Blair
A
,
Sandler
DP
,
Tarone
R
,
Lubin
J
,
Thomas
K
,
Hoppin
JA
, et al
Mortality among participants in the agricultural health study
.
Ann Epidemiol
2005
;
15
:
279
85
.
44.
Szeszenia-Dabrowska
N
,
Wilczynska
U
,
Strzelecka
A
,
Sobala
W
. 
Mortality in the cotton industry workers: results of a cohort study
.
Int J Occup Med Environ Health
1999
;
12
:
143
58
.
45.
Schmidt
C
. 
Immune system's Toll-like receptors have good opportunity for cancer treatment
.
J Natl Cancer Inst
2006
;
98
:
574
5
.
46.
Giron-Gonzalez
JA
,
Moral
FJ
,
Elvira
J
,
Garcia-Gil
D
,
Guerrero
F
,
Gavilan
I
, et al
Consistent production of a higher TH1:TH2 cytokine ratio by stimulated T cells in men compared with women
.
Eur J Endocrinol
2000
;
143
:
31
6
.
47.
Klein
SL
. 
The effects of hormones on sex differences in infection: from genes to behavior
.
Neurosci Biobehav Rev
2000
;
24
:
627
38
.
48.
Pellegrini
P
,
Contasta
I
,
Del Beato
T
,
Ciccone
F
,
Berghella
AM
. 
Gender-specific cytokine pathways, targets, and biomarkers for the switch from health to adenoma and colorectal cancer
.
Clin Dev Immunol
2011
;
2011
:
819724
.
49.
Zeeuwen
PL
,
Boekhorst
J
,
van den Bogaard
EH
,
de Koning
HD
,
van de Kerkhof
PM
,
Saulnier
DM
, et al
Microbiome dynamics of human epidermis following skin barrier disruption
.
Genome Biol
2012
;
13
:
R101
.