The authors have declared that no competing interests exist.
Conceived and designed the experiments: VA UV. Performed the experiments: TIK. Analyzed the data: VA UV RH. Contributed reagents/materials/analysis tools: AT. Wrote the manuscript: VA UV. Obtained funding: VA.
Diet contributes to colorectal cancer development and may be potentially modified. We wanted to identify the biological mechanisms underlying colorectal carcinogenesis by assessment of diet-gene interactions.
The polymorphisms
Genetically determined low COX-2 and high IL-1β activity were associated with increased risk of CRC in this northern Caucasian cohort. Furthermore, interactions were found between
Colorectal cancer (CRC) is one of the most common cancers in the Western World [
Chronic intestinal inflammation is a well-known risk factor for CRC [
IL-10, IL-1β and COX-2 (encoded by
The use of functional polymorphisms has the advantage that the results may allow interpretation of the involved biological pathways in colorectal carcinogenesis.
We have previously assessed diet and
Therefore, we assessed the functional polymorphisms
The Diet, Cancer and Health Study is an ongoing Danish cohort study designed to investigate the relation between diet, lifestyle and cancer risk [
Follow-up was based on population-based cancer registries. Between 1994 and 31th December 2009, nine hundred and seventy CRC cases were diagnosed. A subcohort of 1897 persons was randomly selected within the cohort. Of these, 108 with missing genotype data were excluded. All information on genotypes and diet and lifestyle factors was available for nine hundred and seventy CRC cases and 1789 subcohort members.
Information on diet, lifestyle, weight, height, medical treatment, environmental exposures, and other socio-economic factors were collected at enrolment using questionnaires and interviews. In the food-frequency questionnaire, diet consumption was assessed in 12 categories of predefined responses, ranking from ’never’ to ’eight times or more per day’. The daily intake was then calculated by using FoodCalc [
Contributing food items to the food group ‘cereals’ included wholegrain foods (wholegrain bread, rye bread, wholegrain flour, oatmeal, corncobs, müsli, and crispbread) and refined grain foods (white wheat bread, wheat flour, rice flour, potato flour, corn flour/starch, pasta, wheat) and was measured in grams per day [
Smoking status was classified as never, past or current. Persons smoking at least 1 cigarette daily during the last year were classified as smokers.
The lifestyle questionnaire included this question regarding use of NSAID: “Have you taken more than one pain relieving pill per month during the last year?” If the answer was yes, the participant was asked to record how frequently they took each of the following medications: “Aspirin”, “Paracetamol”, “Ibuprofen”, or “Other pain relievers”. The latter category included NSAID preparations other than aspirin and ibuprofen. Based on all records, we classified study subjects according to use of “any NSAID” (≥ 2 pills per month during one year) at baseline.
Buffy coat preparations were stored at minus 150°C until use. DNA was extracted as described [
Deviation from Hardy-Weinberg equilibrium was assessed using a Chi square test.
Incidence rate ratios (IRR) and 95% Confidence Interval (95%CI) were calculated according to the principles for analysis of case-cohort studies using an un-weighted approach [
All models were adjusted for baseline values of suspected risk factors for colorectal cancer such as body mass index (BMI) (kg/m2, continuous), NSAID (yes/no), use of hormone replacement therapy (HRT) (never/past/current, among women), smoking status (never/past/current), intake of dietary fibre (g/day, continuous), and red meat and processed meat (g/day, continuous). Cereals, fibre, fruit and vegetables were also entered linearly. All analyses were stratified by gender, so that the basic (underlying) hazards were gender specific. For all the polymorphisms, IRR was calculated separately for heterozygous and homozygous variant allele carriers. For all the SNPs except for
Haplotypes of
For the different genes, we investigated possible interactions between the polymorphisms and intake of meat, dietary fibre, cereals, fish, fruit and vegetables, smoking status and NSAID use using the likelihood ratio test [
In another set of interaction analyses between the polymorphisms and the dietary intake subdivided in tertiles, dietary intake was entered as a categorical variable. Tertile cut-points were based on the empirical distribution among cases. The possible interactions were investigated using the likelihood ratio test.
All analyses were performed using R version 2.15-1 (R Core Team, 2013) [
All participants gave verbal and written informed consent. The Diet, Cancer and Health study was approved by the National Committee on Health Research Ethics (journal nr. (KF) 01-345/93) and the Danish Data Protection Agency.
Characteristics of the study population and risk factors for CRC are shown in
Cases |
Sub-cohort |
Test for difference | ||||
---|---|---|---|---|---|---|
No. | Medians | No. | Medians | p-value | ||
(%) | (5-95% percentiles) | (%) | (5-95% percentiles) | |||
Total | 970 (100) | 1789(100) | ||||
Sex | 0.13 | |||||
Men | 547(56) | 954 (53) | ||||
Women | 423(44) | 835 (47) | ||||
Age at inclusion ( |
58 (51-64) | 56 (50-64) | <1e-16 | |||
BMI ( |
26.3 (20.7-34.3) | 25.6 (20.5-33.0) | 0.001 | |||
Food intake ( |
||||||
Alcohol |
14.0 (0.5-69.9) | 13.5 (0.7-65.4) | 0.23 | |||
Dietary fiber | 20.0 (10.6-32.8) | 20.6 (10.8-34.2) | 0.01 | |||
Red and processed meat | 113 (47-233) | 109 (42-236) | 0.03 | |||
Smoking status | 0.07 | |||||
Never | 286 (30) | 603 (34) | ||||
Past | 301 (31) | 518 (29) | ||||
Current | 383 (40) | 667 (37) | ||||
NSAID use | 0.65 | |||||
No | 699 (70) | 1218 (69) | ||||
Yes | 293 (30) | 557 (31) | ||||
HRT use among women |
0.01 | |||||
Never | 258 (61) | 437 (52) | ||||
Past | 55 (13) | 132 (16) | ||||
Current | 110 (26) | 266 (32) |
1 Among current drinkers
2 Percentages among female cases/members of the comparison group
Ncase | Nsub-cohort | Crude |
Adjusted |
P-value |
||||||
---|---|---|---|---|---|---|---|---|---|---|
IRR | (95%CI) |
IRR | (95%CI) |
|||||||
CC | 596 | 1072 | 1.00 | 1.00 | ||||||
AC | 297 | 580 | 0.92 | (0.78-1.10) | 0.92 | (0.77-1.10) | 0.38 | |||
AA | 56 | 96 | 1.02 | (0.71-1.45) | 1.00 | (0.70-1.44) | 0.98 | |||
AC-AA | 353 | 676 | 0.94 | (0.79-1.11) | 0.93 | (0.79-1.11) | 0.44 | |||
CC | 648 | 1200 | 1.00 | 1.00 | ||||||
CT | 263 | 511 | 0.97 | (0.81-1.16) | 0.98 | (0.82-1.18) | 0.87 | |||
TT | 34 | 54 | 1.03 | (0.66-1.63) | 0.99 | (0.62-1.58) | 0.96 | |||
CT-TT | 297 | 565 | 0.98 | (0.82-1.16) | 0.98 | (0.83-1.17) | 0.87 | |||
CC | 336 | 560 | 1.00 | 1.00 | ||||||
CT | 433 | 835 | 0.84 | (0.70-1.01) | 0.82 | (0.68-0.99) | 0.04 | |||
TT | 172 | 351 | 0.79 | (0.63-1.00) | 0.79 | (0.63-1.01) | 0.06 | |||
CT-TT | 605 | 1186 | 0.83 | (0.70-0.98) | 0.81 | (0.68-0.97) | 0.02 | |||
GG | 454 | 925 | 1.00 | 1.00 | ||||||
CG | 408 | 683 | 1.21 | (1.02-1.43) | 1.21 | (1.02-1.44) | 0.03 | |||
CC | 84 | 141 | 1.26 | (0.93-1.71) | 1.30 | (0.95-1.77) | 0.10 | |||
CG-CC | 492 | 824 | 1.21 | (1.03-1.43) | 1.22 | (1.04-1.44) | 0.02 | |||
TT | 389 | 773 | 1.00 | 1.00 | ||||||
TC | 440 | 779 | 1.10 | (0.93-1.31) | 1.11 | (0.93-1.32) | 0.26 | |||
CC | 117 | 204 | 1.22 | (0.94-1.59) | 1.22 | (0.93-1.59) | 0.16 | |||
TC-CC | 557 | 983 | 1.13 | (0.96-1.33) | 1.13 | (0.95-1.33) | 0.16 | |||
AA | 587 | 1126 | 1.00 | 1.00 | ||||||
AG | 313 | 560 | 1.06 | (0.89-1.27) | 1.07 | (0.90-1.28) | 0.43 | |||
GG | 47 | 61 | 1.41 | (0.94-2.11) | 1.46 | (0.97-2.20) | 0.07 | |||
AA-AG vs GG |
900 | 1686 | 1.38 | (0.93-2.05) | 1.42 | (0.95-2.14) | 0.09 | |||
GG | 701 | 1256 | 1.00 | 1.00 | ||||||
GC | 213 | 435 | 0.90 | (0.74-1.09) | 0.86 | (0.71-1.05) | 0.14 | |||
CC | 22 | 43 | 0.91 | (0.54-1.54) | 0.96 | (0.56-1.63) | 0.88 | |||
GC-CC | 235 | 478 | 0.90 | (0.75-1.08) | 0.87 | (0.72-1.05) | 0.15 | |||
TT | 430 | 720 | 1.00 | 1.00 | ||||||
CT | 404 | 815 | 0.86 | (0.72-1.02) | 0.84 | (0.71-1.01) | 0.06 | |||
CC | 97 | 203 | 0.77 | (0.59-1.02) | 0.75 | (0.57-0.99) | 0.04 | |||
CT-CC | 501 | 1018 | 0.84 | (0.71-0.99) | 0.82 | (0.70-0.97) | 0.02 |
a Adjusted for sex and age
b In addition, adjusted for smoking status, alcohol, HRT status (women only), BMI, use of NSAID, and intake of red and processed meat, and dietary fibre
c P-value for the adjusted estimates
d AA and AG versus GG.
TGT | 0 | 330 | 563 | 1 | 1 | ||||||
1 | 424 | 840 | 0.84 | (0.70-1.01) | 0.82 | (0.68-0.99) | 0.035 | ||||
2 | 168 | 353 | 0.78 | (0.62-0.99) | 0.79 | (0.62-1.00) | 0.051 | ||||
CCC | 0 | 444 | 929 | 1 | 1 | ||||||
1 | 397 | 688 | 1.20 | 1.02-1.43) | 1.20 | (1.01-1.43) | 0.040 | ||||
2 | 81 | 139 | 1.25 | (0.92-1.70) | 1.29 | (0.94-1.76) | 0.116 | ||||
GGT | 0 | 560 | 1104 | 1 | 1 | ||||||
1 | 296 | 559 | 1.05 | (0.88-1.25) | 1.06 | (0.89-1.27) | 0.514 | ||||
2 | 46 | 57 | 1.58 | (1.04-2.38) | 1.62 | (1.06-2.47) | 0.024 | ||||
AGT | 0 | 144 | 267 | 1 | 1 | ||||||
1 | 573 | 1110 | 1.01 | (0.80-1.27) | 1.01 | (0.80-1.27) | 0.952 | ||||
2 | 185 | 343 | 1.05 | (0.80-1.39) | 1.05 | (0.79-1.39) | 0.750 |
Haplotype sequence:
a Adjusted for sex and age
b In addition, adjusted for smoking status, alcohol, HRT status (women only), BMI, intake of red and processed meat, and dietary fibre
c P-value for the adjusted risk estimates
Carriers of the high COX-2 activity
Where no recessive effects were observed, variant genotypes were combined in the interaction analysis to maximize the statistical power. A recessive effect was found for
C-592A | CC | 1.03 | (0.99-1.07) | 1.02 | (0.98-1.06) | 0.94 | (0.85-1.04) | 0.94 | (0.85-1.04) | 0.95 | (0.89-1.02) | 0.97 | (0.90-1.04) | ||||||
AC-AA | 1.01 | (0.96-1.07) | 1.00 | (0.95-1.06) | 0.4553 | 0.99 | (0.88-1.12) | 0.98 | (0.86-1.11) | 0.5596 | 0.93 | (0.85-1.02) | 0.95 | (0.86-1.04) | 0.6419 | ||||
rs3024505 | CC | 1.01 | (0.97-1.05) | 1.00 | (0.96-1.04) | 0.91 | (0.83-1.00) | 0.90 | (0.82-0.99) | 0.93 | (0.86-0.99) | 0.94 | (0.87-1.02) | ||||||
CT-TT | 1.06 | (1.01-1.11) | 1.06 | (1.00-1.11) | 0.0361 | 1.08 | (0.94-1.24) | 1.08 | (0.94-1.24) | 0.0065 | 0.98 | (0.90-1.07) | 0.99 | (0.90-1.09) | 0.2715 | ||||
C-3737T | CC | 1.01 | (0.96-1.07) | 1.01 | (0.96-1.07) | 0.98 | (0.86-1.11) | 0.98 | (0.86-1.11) | 0.98 | (0.89-1.08) | 1.00 | (0.90-1.11) | ||||||
CT-TT | 1.03 | (0.99-1.07) | 1.02 | (0.98-1.06) | 0.6519 | 0.94 | (0.86-1.04) | 0.93 | (0.84-1.03) | 0.4590 | 0.92 | (0.86-0.99) | 0.94 | (0.87-1.01) | 0.1743 | ||||
G-1464C | GG | 1.02 | (0.98-1.07) | 1.02 | (0.97-1.06) | 0.95 | (0.86-1.05) | 0.94 | (0.85-1.04) | 0.93 | (0.86-1.01) | 0.95 | (0.88-1.03) | ||||||
GC-CC | 1.03 | (0.98-1.08) | 1.02 | (0.97-1.07) | 0.9367 | 0.97 | (0.86-1.08) | 0.97 | (0.86-1.09) | 0.6227 | 0.95 | (0.88-1.03) | 0.96 | (0.88-1.05) | 0.7630 | ||||
T-31C | TT | 1.01 | (0.96-1.05) | 1.00 | (0.96-1.05) | 0.93 | (0.83-1.03) | 0.91 | (0.81-1.03) | 0.92 | (0.85-1.00) | 0.95 | (0.87-1.03) | ||||||
TC-CC | 1.04 | (0.99-1.08) | 1.03 | (0.98-1.07) | 0.3954 | 0.98 | (0.88-1.08) | 0.98 | (0.88-1.09) | 0.3089 | 0.95 | (0.88-1.03) | 0.96 | (0.89-1.04) | 0.7252 | ||||
A-1195G | AA-AG | 1.02 | (0.99-1.06) | 1.02 | (0.98-1.05) | 0.96 | (0.89-1.04) | 0.96 | (0.8481.04) | 0.95 | (0.90-1.01) | 0.97 | (0.91-1.03) | ||||||
GG | 1.05 | (0.87-1.27) | 1.06 | (0.87-1.29) | 0.5439 | 0.82 | (0.54-1.23) | 0.78 | (0.51-1.19) | 0.2116 | 0.77 | (0.58-1.03) | 0.77 | (0.58-1.04) | 0.0387 | ||||
G-765C | GG | 1.00 | (0.96-1.04) | 0.99 | (0.95-1.03) | 0.93 | (0.85-1.01) | 0.92 | (0.84-1.01) | 0.93 | (0.87-0.99) | 0.94 | (0.88-1.01) | ||||||
GC-CC | 1.08 | (1.01-1.15) | 1.08 | (1.01-1.15) | 0.0058 | 1.05 | (0.90-1.23) | 1.05 | (0.89-1.25) | 0.0663 | 0.99 | (0.89-1.10) | 1.01 | (0.91-1.12) | 0.1771 | ||||
T8473C | TT | 1.04 | (0.99-1.10) | 1.04 | (0.99-1.09) | 0.95 | (0.85-1.05) | 0.94 | (0.84-1.05) | 0.93 | (0.86-1.01) | 0.95 | (0.87-1.03) | ||||||
TC-CC | 1.01 | (0.97-1.06) | 1.01 | (0.96-1.05) | 0.2924 | 0.96 | (0.87-1.07) | 0.95 | (0.85-1.07) | 0.8029 | 0.94 | (0.87-1.02) | 0.96 | (0.88-1.04) | 0.8573 | ||||
C-592A | CC | 0.87 | (0.76-1.00) | 0.91 | (0.78-1.05) | 0.97 | (0.94-1.00) | 0.98 | (0.94-1.01) | 0.98 | (0.93-1.03) | 0.99 | (0.94-1.04) | ||||||
AC-AA | 0.79 | (0.64-0.96) | 0.79 | (0.64-0.98) | 0.1638 | 0.96 | (0.92-1.00) | 0.96 | (0.92-1.00) | 0.3443 | 0.96 | (0.89-1.04) | 0.97 | (0.89-1.04) | 0.5043 | ||||
rs3024505 | CC | 0.75 | (0.65-0.87) | 0.77 | (0.66-0.89) | 0.96 | (0.93-0.99) | 0.96 | (0.93-0.99) | 0.93 | (0.89-0.99) | 0.94 | (0.89-0.99) | ||||||
CT-TT | 1.03 | (0.85-1.25) | 1.06 | (0.87-1.30) | 0.0008 | 0.98 | (0.93-1.03) | 0.99 | (0.94-1.04) | 0.1891 | 1.04 | (0.98-1.11) | 1.06 | (0.99-1.13) | 0.0005 | ||||
C-3737T | CC | 0.81 | (0.66-0.99) | 0.83 | (0.67-1.02) | 0.96 | (0.93-1.00) | 0.96 | (0.92-1.00) | 0.94 | (0.87-1.01) | 0.94 | (0.86-1.02) | ||||||
CT-TT | 0.85 | (0.73-0.98) | 0.88 | (0.75-1.02) | 0.5695 | 0.96 | (0.93-1.00) | 0.97 | (0.94-1.00) | 0.7166 | 0.98 | (0.94-1.03) | 1.00 | (0.95-1.05) | 0.0705 | ||||
G-1464C | GG | 0.83 | (0.71-0.98) | 0.87 | (0.74-1.03) | 0.97 | (0.93-1.00) | 0.97 | (0.94-1.01) | 0.95 | (0.90-1.00) | 0.96 | (0.91-1.02) | ||||||
GC-CC | 0.84 | (0.71-0.99) | 0.85 | (0.72-1.01) | 0.8181 | 0.96 | (0.92-1.00) | 0.96 | (0.92-1.00) | 0.5955 | 1.00 | (0.93-1.06) | 1.00 | (0.94-1.07) | 0.2082 | ||||
T-31C | TT | 0.82 | (0.69-0.97) | 0.86 | (0.72-1.03) | 0.97 | (0.93-1.01) | 0.98 | (0.94-1.01) | 0.96 | (0.91-1.02) | 0.97 | (0.92-1.03) | ||||||
TC-CC | 0.85 | (0.73-0.99) | 0.86 | (0.73-1.01) | 0.9904 | 0.96 | (0.92-0.99) | 0.96 | (0.93-1.00) | 0.4715 | 0.97 | (0.92-1.03) | 0.98 | (0.92-1.05) | 0.8110 | ||||
A-1195G | AA-AG | 0.85 | (0.76-0.96) | 0.88 | (0.78-1.00) | 0.96 | (0.94-0.99) | 0.97 | (0.94-0.99) | 0.97 | (0.93-1.01) | 0.98 | (0.94-1.03) | ||||||
GG | 0.49 | (0.23-1.07) | 0.47 | (0.21-1.05) | 0.0224 | 0.94 | (0.79-1.12) | 0.94 | (0.77-1.12) | 0.6246 | 0.92 | (0.77-1.11) | 0.91 | (0.77-1.09) | 0.3053 | ||||
G-765C | GG | 0.76 | (0.67-0.87) | 0.79 | (0.68-0.91) | 0.95 | (0.92-0.98) | 0.95 | (0.92-0.98) | 0.96 | (0.92-1.01) | 0.98 | (0.93-1.03) | ||||||
GC-CC | 1.13 | (0.90-1.40) | 1.16 | (0.93-1.46) | 0.0003 | 1.02 | (0.96-1.07) | 1.02 | (0.97-1.07) | 0.0041 | 1.01 | (0.93-1.10) | 1.02 | (0.94-1.12) | 0.2457 | ||||
T8473C | TT | 0.76 | (0.63-0.90) | 0.79 | (0.65-0.94) | 0.94 | (0.90-0.97) | 0.94 | (0.91-0.98) | 0.96 | (0.90-1.02) | 0.97 | (0.91-1.03) | ||||||
TC-CC | 0.92 | (0.79-1.07) | 0.94 | (0.80-1.10) | 0.0684 | 0.99 | (0.95-1.02) | 0.99 | (0.96-1.02) | 0.0333 | 0.99 | (0.93-1.05) | 1.00 | (0.94-1.06) | 0.3800 |
a Crude adjusted for sex and age
b Adjusted for smoking status, Alcohol, HRT status (women only), BMI, intake of red and processed meat, and dietary fibre
c P p-value for interaction the adjusted risk estimates
A statistically significant association between
A statistically significant association between
In the present candidate gene study, we analysed gene-environment interactions in relation to risk of CRC in a Danish prospective cohort. We found that functional
We now extend our previous studies of
In the present study, the
The
The intake of meat in the Danish population is among the highest intakes world-wide and we have previously identified interactions between meat and genes [
We observed strong interaction between the marker
We found interaction between NSAID use and
The biological interpretation of our results is supported by other findings. IL-1B, IL-10, and COX-2 are part of the same inflammatory pathways. IL-1 has been found to induce the synthesis of COX-2 through activation of the pro-inflammatory p65 unit of nuclear factor κB (NF-κB) [
Furthermore, our results suggest that those with genetically determined low COX-2 activity are at high risk of CRC by smoking and meat intake and, furthermore, protected by fibre intake. Thus
Taken together, our interaction analyses suggest that diet modify intestinal carcinogenesis through impact on inflammatory response and furthermore suggest that the effect may differ among various populations depending on gene-environment interactions. Our findings should be explored in other well-characterized prospective cohorts.
This study used a nested prospective case-cohort design and has the major advantage that data and samples were collected before diagnosis thus minimizing the risk of differential misclassification between cases and comparison group. The risk estimates were adjusted for known confounding factors affecting risk of CRC in this cohort including dietary factors, body mass index (BMI), alcohol, smoking status and NSAID use. A main strength of the study is the large sample size. The genes were carefully selected based on their role in the inflammatory pathway and the polymorphisms were mainly selected based on their functional effects in order to allow interpretation of the involved biological pathways in colorectal carcinogenesis. Only the interactions between fibre and
We found evidence that genetically determined variation in IL-1β and COX-2 levels is associated with risk of CRC. Moreover, gene-environment interactions suggest that COX-2 and IL10 are implicated in both meat-related carcinogenesis and in the protective effects of fibre in relation to CRC. This study demonstrates that gene-environment interactions provide an efficient tool for identifying factors involved in colorectal carcinogenesis. Our findings should be replicated in other well-characterized prospective cohorts.
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