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Fish Consumption and Colorectal Cancer Risk in Humans: A Systematic Review and Meta-analysis

      Abstract

      Background

      Fish consumption may protect against colorectal cancer, but results from observational studies are inconsistent; therefore, a systematic review with a meta-analysis was conducted.

      Methods

      Relevant studies were identified by a search of MEDLINE and EMBASE databases to May 2011, with no restrictions. Reference lists from retrieved articles also were reviewed. Studies that reported odds ratio (OR) or relative risk estimates with 95% confidence intervals (CIs) for the association between the consumption of fish and the risk of colorectal, colon, or rectal cancer were included. Two authors independently extracted data and assessed study quality. The risk estimate (hazard ratio, relative risk, or OR) of the highest and lowest reported categories of fish intake were extracted from each study and analyzed using a random-effects model.

      Results

      Twenty-two prospective cohort and 19 case-control studies on fish consumption and colorectal cancer risk met the inclusion criteria and were included in the meta-analysis. Our analysis found that fish consumption decreased the risk of colorectal cancer by 12% (summary OR, 0.88; 95% CI, 0.80-0.95). The pooled ORs of colorectal cancer for the highest versus lowest fish consumption in case-control studies and cohort studies were 0.83 (95% CI, 0.72-0.95) and 0.93 (95% CI, 0.86-1.01), respectively. There was heterogeneity among case-control studies (P<.001) but not among cohort studies. A significant inverse association was found between fish intake and rectal cancer (summary OR, 0.79; 95% CI, 0.65-0.97), and there was a modest trend seen between fish consumption and colon cancer (summary OR, 0.96; 95% CI, 0.81-1.14). This study had no publication bias.

      Conclusion

      Our findings from this meta-analysis suggest that fish consumption is inversely associated with colorectal cancer.

      Keywords

      There are more than 1 million newly diagnosed cases
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      • This meta-analysis was conducted of currently available epidemiologic studies on the association between consumption of fish and colorectal cancer to provide a quantitative evaluation in a standardized format on whether fish consumption may decrease the risk of colorectal cancer by 12%.
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      Opposing roles of omega-3 and omega-6 polyunsaturated fatty acids were hypothesized to be behind the beneficial effect of fish consumption in preventing the pathogenesis of t-colorectal cancer.
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      In mice
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      analyzed 18 prospective cohort studies and claimed that the pooled relative risks (RRs) for the highest compared with the lowest fish consumption category were 0.88 (95% CI, 0.78-1.00) for incidence and 1.02 (95% CI, 0.90-1.16) for mortality. More case-control and prospective cohort studies have been performed in recent years,
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      • et al.
      A 22-year prospective study of fish, n-3 fatty acid intake, and colorectal cancer risk in men.
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      Protective effect of fish consumption on colorectal cancer risk Hospital-based case-control study in Eastern Europe.
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      Dietary patterns, food groups, and rectal cancer risk in Whites and African-Americans.
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      A case-control study of roles of diet in colorectal carcinoma in a South Indian Population.
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      • et al.
      Fish consumption and the risk of colorectal cancer: the Ohsaki Cohort Study.
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      Fish consumption and markers of colorectal cancer risk: a multicenter randomized controlled trial.
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      Epidemiology of colorectal cancer in Asia.
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      • Gili M.
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      Dietary changes and colorectal cancer trends in Spain during 1951-2007.
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      Meat, poultry and fish and risk of colorectal cancer: pooled analysis of data from the UK dietary cohort consortium.
      and as such it is necessary to perform a systematic meta-analysis on fish consumption and colorectal cancer risk to more fully elucidate this relationship.
      The current study conducted a meta-analysis of currently available epidemiologic reports that explored the association between consumption of fish and colorectal cancer with the goal of providing a quantitative evaluation in a standardized format that permitted a numeric analysis across studies.

      Materials and Methods

      Data Sources and Searches

      Relevant studies were identified through MEDLINE and EMBASE using a combined text word and Medical Subject Headings (MESH) search strategy with the terms colorectal cancer, colorectal neoplasm, colorectal tumor, colorectal carcinoma, colon cancer, colon neoplasm, colon tumor, colon carcinoma, rectal cancer, rectal neoplasm, rectal tumor, and rectal carcinoma combined with fish. Furthermore, we reviewed the reference lists from retrieved articles to search for more studies.

      Study Selection

      To be included in our meta-analysis, the following criteria had to be met. First, the study had to have a case-control or cohort study design. Second, the exposure of interest was fresh fish consumption. Third, the number of colorectal cancer cases and controls had to be reported. Fourth, the RRs or odds ratios (ORs) with their corresponding 95% confidence interval (CI) for highest versus non/lowest level of fish intake had to be reported.

      Data Extraction and Quality Assessment

      If data were duplicated in more than 1 study, the most recent study was included in the analysis. We identified 46 potentially relevant articles concerning fish consumption and colorectal cancer risk.
      • Kato I.
      • Akhmedkhanov A.
      • Koenig K.
      • et al.
      Prospective study of diet and female colorectal cancer: the New York University Women's Health Study.
      • Norat T.
      • Bingham S.
      • Ferrari P.
      • et al.
      Meat, fish, and colorectal cancer risk: the European Prospective Investigation into cancer and nutrition.
      • Tiemersma E.W.
      • Kampman E.
      • Bueno de Mesquita H.B.
      • et al.
      Meat consumption, cigarette smoking, and genetic susceptibility in the etiology of colorectal cancer: results from a Dutch prospective study.
      • Hall M.N.
      • Chavarro J.E.
      • Lee I.M.
      • et al.
      A 22-year prospective study of fish, n-3 fatty acid intake, and colorectal cancer risk in men.
      • Jedrychowski W.
      • Maugeri U.
      • Pac A.
      • et al.
      Protective effect of fish consumption on colorectal cancer risk Hospital-based case-control study in Eastern Europe.
      • Williams C.D.
      • Satia J.A.
      • Adair L.S.
      • et al.
      Dietary patterns, food groups, and rectal cancer risk in Whites and African-Americans.
      • Nayak S.P.
      • Sasi M.P.
      • Sreejayan M.P.
      • et al.
      A case-control study of roles of diet in colorectal carcinoma in a South Indian Population.
      • Sugawara Y.
      • Kuriyama S.
      • Kakizaki M.
      • et al.
      Fish consumption and the risk of colorectal cancer: the Ohsaki Cohort Study.
      • Ganesh B.
      • Talole S.D.
      • Dikshit R.
      A case-control study on diet and colorectal cancer from Mumbai, India.
      • Spencer E.A.
      • Key T.J.
      • Appleby P.N.
      • et al.
      Meat, poultry and fish and risk of colorectal cancer: pooled analysis of data from the UK dietary cohort consortium.
      • Bostick R.M.
      • Potter J.D.
      • Kushi L.H.
      • et al.
      Sugar, meat, and fat intake, and non-dietary risk factors for colon cancer incidence in Iowa women (United States).
      • Busstra M.C.
      • Siezen C.L.
      • Grubben M.J.
      • et al.
      Tissue levels of fish fatty acids and risk of colorectal adenomas: a case-control study (Netherlands).
      • Chiu B.C.
      • Ji B.T.
      • Dai Q.
      • et al.
      Dietary factors and risk of colon cancer in Shanghai, China.
      • Diergaarde B.
      • Tiemersma E.W.
      • Braam H.
      • et al.
      Dietary factors and truncating APC mutations in sporadic colorectal adenomas.
      • Engeset D.
      • Andersen V.
      • Hjartåker A.
      • et al.
      Consumption of fish and risk of colon cancer in the Norwegian Women and Cancer (NOWAC) study.
      • English D.R.
      • MacInnis R.J.
      • Hodge A.M.
      • et al.
      Red meat, chicken, and fish consumption and risk of colorectal cancer.
      • Fernandez E.
      • Chatenoud L.
      • La Vecchia C.
      • et al.
      Fish consumption and cancer risk.
      • Franceschi S.
      • Favero A.
      The role of energy and fat in cancers of the breast and colon-rectum in a southern European population.
      • Gaard M.
      • Tretli S.
      • Løken E.B.
      Dietary factors and risk of colon cancer: a prospective study of 50,535 young Norwegian men and women.
      • Giovannucci E.
      • Rimm E.B.
      • Stampfer M.J.
      • et al.
      Intake of fat, meat, and fiber in relation to risk of colon cancer in men.
      • Hsing A.W.
      • McLaughlin J.K.
      • Chow W.H.
      • et al.
      Risk factors for colorectal cancer in a prospective study among U.S. white men.
      • Hu J.
      • La Vecchia C.
      • DesMeules M.
      • et al.
      Canadian Cancer Registries Epidemiology Research Group Meat and fish consumption and cancer in Canada.
      • Iscovich J.M.
      • L'Abbé K.A.
      • Castelleto R.
      • et al.
      Colon cancer in Argentina I: Risk from intake of dietary items.
      • Kampman E.
      • Slattery M.L.
      • Bigler J.
      • et al.
      Meat consumption, genetic susceptibility, and colon cancer risk: a United States multicenter case-control study.
      • Kato I.
      • Tominaga S.
      • Matsuura A.
      • et al.
      A comparative case-control study of colorectal cancer and adenoma.
      • Khan M.M.
      • Goto R.
      • Kobayashi K.
      • et al.
      Dietary habits and cancer mortality among middle aged and older Japanese living in Hokkaido, Japan by cancer site and sex.
      • Kimura Y.
      • Kono S.
      • Toyomura K.
      • et al.
      Meat, fish and fat intake in relation to subsite-specific risk of colorectal cancer: The Fukuoka Colorectal Cancer Study.
      • Knekt P.
      • Järvinen R.
      • Dich J.
      • et al.
      Risk of colorectal and other gastro-intestinal cancers after exposure to nitrate, nitrite and N-nitroso compounds: a follow-up study.
      • Kojima M.
      • Wakai K.
      • Tamakoshi K.
      • et al.
      Japan Collaborative Cohort Study Group
      Diet and colorectal cancer mortality: results from the Japan Collaborative Cohort Study.
      • Larsson S.C.
      • Rafter J.
      • Holmberg L.
      • et al.
      Red meat consumption and risk of cancers of the proximal colon, distal colon and rectum: the Swedish Mammography Cohort.
      • Le Marchand L.
      • Wilkens L.R.
      • Hankin J.H.
      • et al.
      A case-control study of diet and colorectal cancer in a multiethnic population in Hawaii (United States): lipids and foods of animal origin.
      • Lee S.A.
      • Shu X.O.
      • Yang G.
      • et al.
      Animal origin foods and colorectal cancer risk: a report from the Shanghai Women's Health Study.
      • Lüchtenborg M.
      • Weijenberg M.P.
      • de Goeij A.F.
      • et al.
      Meat and fish consumption, APC gene mutations and hMLH1 expression in colon and rectal cancer: a prospective cohort study (The Netherlands).
      • Ma J.
      • Giovannucci E.
      • Pollak M.
      • et al.
      Milk intake, circulating levels of insulin-like growth factor-I, and risk of colorectal cancer in men.
      • Pietinen P.
      • Malila N.
      • Virtanen M.
      • et al.
      Diet and risk of colorectal cancer in a cohort of Finnish men.
      • Ramadas A.
      • Kandiah M.
      Food intake and colorectal adenomas: a case-control study in Malaysia.
      • Sanjoaquin M.A.
      • Appleby P.N.
      • Thorogood M.
      • et al.
      Nutrition, lifestyle and colorectal cancer incidence: a prospective investigation of 10998 vegetarians and non-vegetarians in the United Kingdom.
      • Steinmetz K.A.
      • Potter J.D.
      Food-group consumption and colon cancer in the Adelaide Case-Control Study II. Meat, poultry, seafood, dairy foods and eggs.
      • Willett W.C.
      • Stampfer M.J.
      • Colditz G.A.
      • et al.
      Relation of meat, fat, and fiber intake to the risk of colon cancer in a prospective study among women.
      • Yang C.X.
      • Takezaki T.
      • Hirose K.
      • et al.
      Fish consumption and colorectal cancer: a case-reference study in Japan.
      • Zhang B.
      • Li X.
      • Nakama H.
      • et al.
      A case-control study on risk of changing food consumption for colorectal cancer.
      • Brink M.
      • Weijenberg M.P.
      • de Goeij A.F.
      • et al.
      Meat consumption and K-ras mutations in sporadic colon and rectal cancer in The Netherlands Cohort Study.
      • Diergaarde B.
      • van Geloof W.L.
      • van Muijen G.N.
      • et al.
      Dietary factors and the occurrence of truncating APC mutations in sporadic colon carcinomas: a Dutch population-based study.
      • Franceschi S.
      • Favero A.
      • La Vecchia C.
      • et al.
      Food groups and risk of colorectal cancer in Italy.
      • Goldbohm R.A.
      • van den Brandt P.A.
      • van't Veer P.
      • et al.
      A prospective cohort study on the relation between meat consumption and the risk of colon cancer.
      • Inoue M.
      • Tajima K.
      • Hirose K.
      • et al.
      Subsite-specific risk factors for colorectal cancer: a hospital-based case-control study in Japan.
      After the duplicated studies were excluded, the remaining publications in the meta-analysis of fish and colorectal cancer included 41 articles: 22 cohort studies and 19 case-control studies. We used a standardized protocol and reporting form to abstract the following data from each publication: reference (first author, year of publication), study design, population used in the case-control study (population-based case-control or hospital-based case-control), gender of subjects and location of cancer (colon cancer, rectal cancer, and colorectal cancer), country in which the study was performed, number of cases and controls, lowest and highest level of fish consumption, RRs or ORs with 95% CIs for colorectal cancer associated with fish intake, and covariate adjustment.

      Data Synthesis and Analysis

      Study-specific ORs/RRs and corresponding 95% CIs for highest versus non/lowest fish consumption levels were extracted. For case-control studies, the proportions (expressed as percentages) of control subjects in the highest and non/lowest consumption categories were reported. For cohort studies, the percentages of subjects in the highest and non/lowest consumption levels were calculated as the proportion of the number of subjects in these 2 categories over the total number of study subjects or as the proportion of person-years in these 2 categories over the total person-years.
      Q and Higgins I2 statistics were used to examine heterogeneity among the studies included in this meta-analysis.
      • DerSimonian R.
      • Laird N.
      Meta-analysis in clinical trials.
      • Higgins J.P.
      • Thompson S.G.
      • Deeks J.J.
      • et al.
      Measuring inconsistency in meta-analyses.
      For the Q statistics, a P value less than .10 indicated statistically significant heterogeneity. We defined statistical significance as P<.10 rather than the conventional level of .05 because of the low power of this test.
      • Hedges L.V.
      • Pigott T.D.
      The power of statistical tests in meta-analysis.
      I2 lies between 0% (no observed heterogeneity) and 100% (maximal heterogeneity), and an I2 value greater than 50% may be considered to represent substantial heterogeneity. We used the fixed effect model to calculate the summary ORs and its 95% CI among studies with homogeneous results, whereas the random-effect model proposed by DerSimonian and Laird was used to calculate summary ORs when a significant heterogeneity was found. We calculated summary estimates for the 2 study types (case-control and cohort) separately and in combination.
      To assess the potential for publication bias, we used funnel plots and Egger's regression.
      • Thornton A.
      • Lee P.
      Publication bias in meta-analysis: its causes and consequences.
      • Egger M.
      • Davey Smith G.
      • et al.
      Bias in meta-analysis detected by a simple, graphical test.
      All statistical analyses were performed with STATA (v 10.0; StataCorp, College Station, Tex).

      Results

      Literature Search

      The detailed steps of our literature search are shown in Figure 1. Briefly, we identified 46 potentially relevant articles concerning fish consumption and colorectal cancer. Five articles were excluded because of duplicated reports from the same study population. In the end, 41 studies were included in this meta-analysis.
      Figure thumbnail gr1
      Figure 1Selection of studies for inclusion in meta-analysis.

      Study Characteristics

      The 41 articles that met our inclusion criteria in this meta-analysis were published between 1990 and 2011. There were 22 cohort studies and 19 case-control studies (10 population-based and 9 hospital-based case-control studies).

      Sensitive Analysis

      According to the highest and lowest level of fish consumption reported, the RRs or ORs for each of the studies, along with their summary ORs, are shown in Table 1 and Figure 2. Significant heterogeneity was found in the results across the 41 studies (Q=131.97, P<.001, I2 = 56.80%). The summary ORs among all studies showed a significant reduction in risk of colorectal cancer in those populations with high fish consumption intake (summary OR, 0.88; 95% CI, 0.80-0.95).
      Table 1Characteristics of Studies on Fish Consumption and Colorectal Cancer
      StudyDesignCasesStudy Population/ControlsGender of SubjectsLocation of CancerPopulationRange of ExposureOR or RR (95% CI)Adjustments
      Bostick et al 1994Cohort21235,215FemaleColonUSA<1 time/wk vs >2.5 times/wk, Q1 vs Q50.76 (0.49-1.18)Age, energy intake, height, parity, vitamin E, vitamin E*age interaction, and vitamin A
      Engeset et al 2007Cohort25463,914FemaleColonNorway<70.8 g/d vs >117 g/d, T1 vs T31.28 (0.9-1.81)Age, daily intake of energy, smoking, fish liver, fruit and vegetables, fiber, fats, and sauces
      English et al 2004Cohort45137,112BothColorectalAustralia<1 times/wk vs ≥2.5 times/wk,Q1 vs Q40.9 (0.7-1.2)Age, energy intake, country of birth, gender, fat, and cereal intake
      Gaard et al 1996Cohort5624,897FemaleColorectalNorway<2 meals/wk vs ≥5 meals/wk0.81 (0.32-2.06)Age
      Gaard et al 1996Cohort8725,638MaleColorectalNorway<2 meals/wk vs ≥5 meals/wk0.46 (0.19-1.11)Age
      Giovannucci et al 1994Cohort20547,949MaleColonUSA8.4 g/d vs 83.4 g/d1.06 (0.7-1.6)Age and total energy intake
      Hall et al 2008Cohort50021,406MaleColorectalUSA<1 time/wk vs ≥5 times/wk0.63 (0.42-0.95)Age, smoking, BMI, multivitamin use, history of diabetes, random assignment to aspirin or placebo, vigorous exercise, alcohol, and red meat intake
      Hsing et al 1998Cohort14517,633MaleColorectalUSA<0.8 time/mo vs >4 times/mo, Q1 vs Q41.5 (0.9-2.6)Age, calories, smoking, alcohol intake, and total energy
      Kato et al 1997Cohort10014,727FemaleColorectalUSAQ1 vs Q40.49 (0.27-0.89)Age, total energy intake, education, and place of residence
      Khan et al 2004Cohort141634FemaleColorectalJapanNever+several times/year+several times/mo vs several times/wk+daily1.2 (0.4-3.7)Age, health status, health education, health screening, and smoking
      Khan et al 2004Cohort151524MaleColorectalJapanNever+several times/y+several times/mo vs several times/wk+daily0.5 (0.2-1.4)Age and smoking
      Knekt et al 1999Cohort739985BothColorectalFinlandQ1 vs Q41.11 (0.55-2.28)Age, energy intake, gender, municipality, and smoking
      Kojima et al 2004Cohort11645,181MaleRectumJapan3-7 times/wk vs 0-2 times/mo0.95 (0.6-1.51)Age, family history, BMI, smoking, physical activity, education, alcohol intake, and region
      Kojima et al 2004Cohort13845,181MaleColonJapan3-7 times/wk vs 0-2 times/mo1.04 (0.65-1.66)Age, family history, BMI, smoking, physical activity, education, alcohol intake, and region
      Kojima et al 2004Cohort14662,643FemaleColonJapan3-7 times/wk vs 0-2 times/mo0.97 (0.62-1.5)Age, family history, BMI, smoking, physical activity, education, alcohol intake, and region
      Kojima et al 2004Cohort5762,643FemaleRectumJapan3-7 times/wk vs 0-2 times/mo0.9 (0.44-1.84)Age, family history, BMI, smoking, physical activity, education, alcohol intake, and region
      Larsson et al 2005Cohort73361,433FemaleColorectalSweden<0.5 servings/wk vs ≥2 servings/wk1.08 (0.81-1.43)Age, energy, education, BMI, alcohol, saturated fat, calcium, fruits and vegetables, whole-grain foods, red meat, and poultry
      Lee et al 2009Cohort39473,224FemaleColorectalChina<20 g/d vs ≥74 g/d, Q1 vs Q51.3 (0.9-1.9)Age, education, income, survey season, tea consumption, NSAID use, energy intake, and fiber intake
      Lüchtenborg et al 2005Cohort1542948BothRectumThe Netherlands0 g/d vs 28.2 g/d, Q1 vs Q40.94 (0.59-1.52)Age, energy intake, gender, family history of colorectal cancer, smoking, and BMI
      Lüchtenborg et al 2005Cohort4342948BothColonThe Netherlands0 g/d vs 28.2 g/d, Q1 vs Q41.03 (0.76-1.4)Age, energy intake, gender, family history of colorectal cancer, smoking, and BMI
      Ma et al 2001Cohort19314,916MaleColorectalUSA≤0.14 servings/d vs 0.35-2.03 servings/d, T1 vs T30.92 (0.56-1.51)Age, smoking, BMI, alcohol, multivitamin, aspirin, exercise, and molar ratio of IGF-I to IGFBP-3
      Norat et al 2005Cohort1329478,040BothColorectal10 European countries<10 g/d vs ≥80 g/d0.69 (0.54-0.88)Age, energy intake, gender, height, weight, occupational physical activity, smoking, dietary fiber, alcohol, and center
      Pietinen et al 1999Cohort18527,111MaleColorectalFinland13 g/d vs 68 g/d, Q1 vs Q40.9 (0.6-1.4)Age, education, smoking, BMI, alcohol, physical activity, and calcium intake
      Sanjoaquin et al 2004Cohort9510,998BothColorectalUK0 times/wk vs ≥1 time/wk1.17 (0.71-1.92)Age, gender, smoking, and alcohol
      Spencer et al 2010Cohort5792575BothColorectalUK<1 g/d vs ≥30 g/d0.86 (0.64-1.16)Age, height, weight, smoking, energy, alcohol, and dietary fiber
      Sugawara et al 2009Cohort18718,858FemaleColorectalJapanQ1 vs Q40.96 (0.61-1.53)Age, BMI, family history of cancer, history of stroke, hypertension, myocardial infarction and diabetes mellitus, education, marital status, job status, smoking, alcohol, time spent walking, total calories, and fruit and vegetables
      Sugawara et al 2009Cohort37920,640MaleColorectalJapanQ1 vs Q41.07 (0.78-1.46)Age, BMI, family history of cancer, history of stroke, hypertension, myocardial infarction and diabetes mellitus, education, marital status, job status, smoking, alcohol, time spent walking, total calories, and fruit and vegetables
      Tiemersma et al 2002Cohort102537BothColorectalThe Netherlands0-1 time/mo vs >4 times/mo0.7 (0.4-1.3)Age, gender, center, total energy intake, alcohol, and height
      Willett et al 1990Cohort15088,751FemaleColonUSA<1 times/mo vs ≥17 times/mo1.06 (0.36-3.12)Age
      Yang et al 2003HCC39232,285FemaleColonJapan<1 time/wk vs >4 times/wk0.8 (0.52-1.24)Age, season and year of questionnaire study, family history of colorectal cancer, smoking, alcohol, physical exercise, vegetables, and meat
      Yang et al 2003HCC21232,285FemaleRectumJapan<1 time/wk vs >4 times/wk0.62 (0.33-1.16)Age, season and year of questionnaire study, family history of colorectal cancer, smoking, alcohol, physical exercise, vegetables, and meat
      Yang et al 2003HCC53614,601MaleColonJapan<1 time/wk vs >4 times/wk0.68 (0.47-0.99)Age, season and year of questionnaire study, family history of colorectal cancer, smoking, alcohol, physical exercise, vegetables and meat
      Yang et al 2003HCC41014,601MaleRectumJapan<1 time/wk vs >4 times/wk1.13 (0.76-1.68)Age, season and year of questionnaire study, family history of colorectal cancer, smoking, alcohol, physical exercise, vegetables, and meat
      Busstra et al 2003HCC5257BothColorectalThe Netherlands<1 time/mo vs >1 time/wk0.5 (0.2-1.6)Energy intake, age, familial background, and gender
      Chiu et al 2003PCC469701FemaleColonChinaQ1 vs Q41.2 (0.8-1.7)Age, total energy, education, BMI, income, and occupational physical activity
      Chiu et al 2003PCC462851MaleColonChinaQ1 vs Q41.7 (1.2-2.4)Age, total energy, education, BMI, income, and occupational physical activity
      Diergaarde et al 2005PCC278414BothColorectalThe Netherlands<7 g/d vs >19 g/d, T1 vs T30.9 (0.6-1.3)Age, gender, and energy intake
      Fernandez et al 1999HCC8287990BothColonItaly<1 serving/wk vs ≥2 servings/wk0.6 (0.5-0.7)Age, gender, area of residence, education, smoking, alcohol, and BMI
      Fernandez et al 1999HCC4987990BothRectumItaly<1 serving/wk vs ≥2 servings/wk0.5 (0.3-0.6)Age, gender, area of residence, education, smoking, alcohol, and BMI
      Franceschi and Favero 1999HCC19534154BothColorectalItalyQ1 vs Q50.72 (0.59-0.88)Age, gender, center, education, physical activity, and total energy intake
      Ganesh et al 2009HCC2031628BothColorectalIndiaNo vs yes0.6 (0.4-0.9)Age, place of residence, vegetables intake, dry fish and meat, religion, and occupation
      Hu et al 2008PCC17275039BothColonCanadaQ1 vs Q41 (0.8-1.3)Age, province, education, BMI, gender, alcohol use, smoking, vegetable and fruit intake, and total energy intake
      Hu et al 2008PCC14475039BothRectumCanadaQ1 vs Q40.9 (0.7-1.2)Age, province, education, BMI, gender, alcohol use, smoking, vegetable and fruit intake, and total energy intake
      Iscovich et al 1992PCC110220BothColonArgentinaQ1 vs Q40.39 (0.15-1.05)NA
      Jedrychowski et al 2008HCC584745BothColorectalPolandQ1 vs Q40.71 (0.51-0.98)Gender, age, marital status, residence area, BMI, physical activity, and smoking
      Kampman et al 1995HCC232259BothColonUSA<5 g/d vs >24 g/d, Q1 vs Q41.13 (0.68-1.87)Age, gender, urbanization level, energy intake, alcohol, cholecystectomy, and family history of colon cancer
      Kato et al 1990PCC132578BothColonJapanLess than daily intake vs daily intake0.95 (0.63-1.43)Age, gender, and residence
      Kato et al 1990PCC91578BothRectumJapanLess than daily intake vs daily intake0.88 (0.55-1.43)Age, gender, and residence
      Kimura et al 2007PCC782793BothColorectalJapanQ1 vs Q50.8 (0.57-1.13)Age, gender, dietary fiber and calcium, physical activity, smoking, alcohol, residence, parental colorectal cancer, and type of job
      Le Marchand et al 1997PCC494494FemaleColorectalAsiaQ1 vs Q41.1 (0.6-1.7)Age, family history of colorectal cancer, alcohol, recreational activity, BMI, and caloric, dietary fiber, and calcium intakes
      Le Marchand et al 1997PCC698698MaleColorectalAsiaQ1 vs Q41.1 (0.7-1.6)Age, family history of colorectal cancer, alcohol, recreational activity, BMI, and caloric, dietary fiber, and calcium intakes
      Nayak et al 2009HCC108324BothColorectalIndiaT1 vs T30.09 (0.03-0.28)NA
      Ramadas and Kandiah 2009HCC5959BothColorectalMalaysia<3 times/wk vs ≥3 times/wk1.1 (0.42-2.9)Age, ethnicity, gender, physical activity, height, BMI, waist circumference, energy, alcohol, and smoking
      Steinmetz and Potter 1993PCC99438FemaleColonAustralia≤0.3 g/d vs ≥1.1 g/d, Q1 vs Q41.56 (0.79-3.07)Age at first live birth, Quetelet index, and alcohol
      Steinmetz and Potter 1993PCC121438MaleColonAustralia≤0.3 g/d vs ≥1.1 g/d, Q1 vs Q40.74 (0.37-1.49)Occupation, Quetelet index, and alcohol
      Williams et al 2009PCC225159BothRectumUSA African-AmericansQ1 vs Q41.14 (0.51-2.54)Age, gender, education, income, BMI, physical activity, family history, NSAID use, and total energy intake
      Williams et al 2009PCC720800BothRectumUSA whiteQ1 vs Q40.52 (0.36-0.73)Age, gender, education, income, BMI, physical activity, and family history, nonsteroidal anti-inflammatory drug use, and total energy intake
      Zhang et al 2002HCC10299BothColorectalChinaNever vs 2 times/d1.52 (0.64-3.59)NA
      PCC=population-based case-control study; HCC=hospital-based case-control study; Q=quantile; T=tertile; OR=odds ratio; RR=relative risk; CI=confidence interval; BMI=body mass index; NSAID=nonsteroidal anti-inflammatory drug; IGF-I=insulin-like growth factor-1; IGFBP-3=insulin-like growth factor binding protein-3.
      Figure thumbnail gr2
      Figure 2Consumption of fish in association with colorectal cancer risk. Each study specific point estimate is plotted as a square box. The size of the box is proportional to the precision of the estimate, and its 95% CI is denoted by a horizontal line through the box. The vertical dashed line and the lower vertex of the diamond indicate the combined risk estimate of the analysis, and the left and right vertices of the diamond represent its 95% confidence limits. Results of the analysis using the random-effects model yielded a combined risk estimate of 0.88 (95% CI, 0.80-0.95; Q=131.97, P<.001, I2 = 56.80%). CI=confidence interval; OR=odds ratio.
      By stratifying by case-control and prospective cohort studies, significant heterogeneity was found within the 19 case-control studies (Q=89.69, P<.001, I2 = 68.8%), and a significant association between fish intake and colorectal cancer risk also was observed (summary OR, 0.83; 95% CI, 0.72-0.95) (Supplemental Figure 1A). In cohort studies, there was no significant heterogeneity (Q=33.49, P=.22, I2 = 16.4%), and a slight association was found between fish consumption and colorectal cancer (summary OR, 0.93; 95% CI, 0.86-1.01) (Supplemental Figure 1B). When we stratified the various studies by design (case-control vs cohort), there was no significant heterogeneity between the 2 types of study designs (case-control vs cohort studies, Q=1.94, P=.16, I2 = 48.40%) (Table 2).
      Table 2Results of Stratified Analysis on the Basis of Study Types, Study Populations, Location of Colorectal Cancer, and Incidence/Mortality of Cancer Risk
      Category of StudiesNo. of StudiesSummary OR or RR (95% CI)P for HeterogeneityPublication Bias
      All studies410.87 (0.80-0.95)<.001No
      Study types
      Case-control studies190.83 (0.72-0.95)<.001No
      Cohort studies220.93 (0.86-1.01).22No
      Case-control studies vs cohort studies.16
      Types of case-control studies
      HCC100.70 (0.59-0.84).001No
      PCC90.96 (0.81-1.13).004No
      HCC vs PCC.01
      Location of colorectal cancer
      Colon cancer140.96 (0.81-1.14)<.001No
      Rectum cancer70.79 (0.65-0.97).026No
      Cancer risk
      Cancer incidence380.86 (0.79-0.95)<.001No
      Cancer mortality31.01 (0.81-1.26).50No
      PCC=population-based case-control study; HCC=hospital-based case-control study; OR=odds ratio; RR=relative risk; CI=confidence interval.
      In case-control studies, the design could be further divided into hospital-based and population-based studies. By stratifying the 2 subgroups of case-control studies, significant heterogeneity was found within hospital-based (Q=33.91, P=.001, I2 = 61.70%) and population-based case-control studies (Q=31.65, P=.004, I2 = 55.8%). At the same time, significant heterogeneity was found between the 2 subgroups of case-control studies (Q=6.51, P=.01, I2 = 84.60%) (Table 2). In the hospital-based case-control studies, the summary OR among hospital-based case-control studies showed a significant reduction in risk of colorectal cancer with high fish consumption (summary OR, 0.70; 95% CI, 0.59-0.84) (Supplemental Figure 2A). Although the reduction in risk among population-based case-control studies was less dramatic than that seen above. There was still a trend toward an inverse relationship between fish intake and colorectal carcinogenesis (summary OR, 0.96; 95% CI, 0.81-1.13) (Supplemental Figure 2B).
      To explore the source of the heterogeneity seen in the case-control studies, we further conducted analyses stratified by type of fish, area of residence (seaside or not), level of adjustment of overall diet or lifestyle, and other factors.
      In the results presented above, we selected only the data of fresh fish. We explored the association between processed fish and colorectal cancer. The results indicated no significant heterogeneity (Q=4.89, P=.56, I2 = 0), whereas at the same time, there was a marginal increase in risk between intake of processed fish and colorectal cancer (summary OR, 1.12; 95% CI, 0.98-1.30).
      The case-control studies were further stratified by the area of residence. Among 4 studies in which the population lived seaside, no significant heterogeneity was found (Q=5.36, P=.50, I2 = 0), and there was a significant protective effect of fish consumption on colorectal cancer noted (summary OR, 0.82; 95% CI, 0.69-0.96). Among the 9 studies in which the population did not live seaside, significant heterogeneity was observed (Q=50.04, P<.001, I2 = 78.0%), and the association between fish use and colorectal cancer was not statistically significant (summary OR, 0.87; 95% CI, 0.67-1.14).
      These case-control studies were stratified by adjustment of overall diet to reflect total energy input and fruit, vegetables, or dietary fiber intake. Among the 9 studies that were adjusted for energy intake, significant heterogeneity was found among them (Q=33.77, P=.001, I2 = 64.5%), and no significant association was found between fish consumption and colorectal cancer (summary OR, 0.96; 95% CI, 0.80-1.15). Among the 6 studies that were adjusted for intake of fruit, vegetables, or dietary fiber, no significant heterogeneity was found (Q=12.30, P=.27, I2 = 18.7%), and a significant association between fish consumption and colorectal cancer was observed (summary OR, 0.89; 95% CI, 0.80-0.99).
      We then stratified these case-control studies to adjust for lifestyle including the level of physical activity, smoking, and alcohol use. Of the 9 studies that were adjusted for physical activity, significant heterogeneity was found among them (Q=38.72, P<.001, I2 = 63.8%), and a slight protective effect was found between fish consumption and colorectal cancer (summary OR, 0.90; 95% CI, 0.76-1.07). Seven studies were adjusted for smoking, and significant heterogeneity was found among them (Q=27.35, P=.004, I2 = 59.8%). Moreover, the inverse association was found between fish consumption and colorectal cancer in these studies adjusted for smoking (summary OR, 0.78; 95% CI, 0.67-0.92). Within the 7 studies adjusted for alcohol, significant heterogeneity was noted (Q=34.03, P=.001, I2 = 61.8%), and a significant relationship between fish consumption and colorectal cancer was found (summary OR, 0.83; 95% CI, 0.70-0.98).
      Last, these case-control studies were stratified to adjust for potential risk factors, including gender, body mass index (BMI), and family history of cancer. Ten studies were adjusted for gender within which significant heterogeneity (Q=28.12, P=.009, I2 = 53.8%) and a significant inverse association were found between fish consumption and colorectal cancer (summary OR, 0.75; 95% CI, 0.66-0.86). Among the 8 studies that were adjusted for BMI, significant heterogeneity was found among them (Q=55.99, P<.001, I2 = 78.6%), and no significant association was found between fish consumption and colorectal cancer (summary OR, 0.87; 95% CI, 0.71-1.07). For the 4 studies that were adjusted for family history of cancer, significant heterogeneity was found among them (Q=14.43, P=.071, I2 = 44.5%), and a marginal therapeutic benefit was noted for the effect of fish consumption on the carcinogenesis of colorectal cancer (summary OR, 0.82; 95% CI, 0.68-1.00).
      Despite the fact that colon and rectal cancers share many features and are often referred to as “colorectal cancer,” these 2 cancer types tend to demonstrate many different characteristics.
      • Li M.
      • Li J.Y.
      • Zhao A.L.
      • et al.
      Colorectal cancer or colon and rectal cancer? Clinicopathological comparison between colonic and rectal carcinomas.
      • Kalady M.F.
      • Sanchez J.A.
      • Manilich E.
      • et al.
      Divergent oncogenic changes influence survival differences between colon and rectal adenocarcinomas.
      As a result, it might be worthwhile to investigate whether there is a relationship between fish consumption and colon or rectal cancer, respectively. For the studies stated “colorectal cancer,” some combined both the colon cancer and rectal cancer, whereas there were studies that solely referred to colorectal cancer, so these studies were excluded. Significant heterogeneity was found within 14 colon cancer studies (Q=53.53, P<.001, I2 = 68.20%), which showed a slight reduction of colon cancer risk with fish intake (summary OR, 0.96; 95% CI, 0.81-1.14) (Supplemental Figure 3A). In 7 rectal cancer studies, there was significant heterogeneity (Q=18.89, P=.03, I2 = 52.30%), and a significant decrease was found between fish consumption and risk of rectal cancer (summary OR, 0.79; 95% CI, 0.65-0.97) (Supplemental Figure 3B) (Table 2).
      We then stratified the studies by cancer incidence and mortality. Among the 3 mortality studies, no significant heterogeneity was found (Q=4.36, P=.50, I2 = 0), and no significant association between fish intake and colorectal cancer was found (summary OR, 1.01; 95% CI, 0.82-1.26) (Supplemental Figure 4A). Within the 38 incidence studies, significant heterogeneity was found (Q=125.03, P<.001, I2 = 59.20%) and a significant association between fish consumption and colorectal cancer was observed (summary OR, 0.86; 95% CI, 0.79-0.95) (Supplemental Figure 4B) (Table 2).

      Publication Bias Analysis

      As depicted in a symmetric Begg's funnel plot (Figure 3), no publication bias was found in this study (P=.55). Furthermore, there was no evidence of bias detected by Egger's test (intercept=0.37, P=.45).
      Figure thumbnail gr3
      Figure 3Forest plot of the RRs of colorectal cancer incidence or mortality and fish consumption (highest compared with the lowest category) in population-based case-control studies. As depicted in a symmetric Begg's funnel plot with intercept=0.37 and P=.45, no evidence of bias was detected in this meta-analysis by Egger's test.

      Discussion

      The role that diet plays in preventing cancer has drawn more and more attention in recent years. This meta-analysis, which includes 22 prospective cohort and 19 case-control studies, evaluated the strength of current evidence that suggests that fish consumption may reduce the risk of colorectal cancer by as much as 12%. The strength of the present meta-analysis is that detailed information is provided in the study design, including selection criteria for cases and controls and methods of data collection, and there also is the considerable number of studies and subjects. The relationship between fish consumption and risk of colorectal cancer within subtypes of the studies (eg, prospective cohort and case-control studies, hospital-based and population-based control studies, colon cancer and rectal cancer, colorectal cancer incidence and mortality) were thoroughly analyzed in this meta-analysis, which might provide specific evidence on this topic.
      Among the 41 epidemiologic studies we included, one study showed an extremely significant protective effect of fish consumption in colon cancer incidence.
      • Nayak S.P.
      • Sasi M.P.
      • Sreejayan M.P.
      • et al.
      A case-control study of roles of diet in colorectal carcinoma in a South Indian Population.
      Because the study design met our criteria of analysis, we did not remove the report from our investigation. However, we thought about the potential effect that this study might have on the bias of our meta-analysis, and as such, we performed another meta-analysis without this study. The results from this additional analysis also demonstrated a significant relationship between the consumption of fish and the reduction of risk in colon cancer (summary OR, 0.88; 95% CI, 0.82-0.96; Q=116.38, P<.001, I2 = 51.90%).
      In our meta-analysis, a more significant association between fish intake and colorectal cancer risk abatement was observed (summary OR, 0.83; 95% CI, 0.72-0.95) from the 19 case-control studies, whereas less of a decrease in risk was found from the 22 prospective cohort studies (summary OR, 0.93; 95% CI, 0.86-1.01). Generally, prospective cohort studies are thought to provide more of a quantitative assessment because they have the potential of minimizing the possibility of bias due to recall or selection that is typically noted in case-control studies.
      • Larsson S.C.
      • Orsini N.
      • Wolk A.
      Vitamin B6 and risk of colorectal cancer: a meta-analysis of prospective studies.
      This study was based on both case-control and prospective cohort studies, which might provide more practical results for meta-analysis. Because results from case-control studies demonstrated significant heterogeneity among them, we conducted further stratified analyses to explore the source of this heterogeneity.
      It is well known that therapeutic strategies for colon cancer and rectal cancer differ,
      • Cunningham D.
      • Atkin W.
      • Lenz H.J.
      • et al.
      Colorectal cancer.
      even though colon cancer and rectal cancer share some similarities. For instance, the colon originates embryologically from the midgut and hindgut, whereas the rectum originates from the cloaca.
      • Araki K.
      • Furuya Y.
      • Kobayashi M.
      • et al.
      Comparison of mucosal microvasculature between the proximal and distal human colon.
      Because of the differences between colon and rectal tissue and the differences between these 2 cancer types, a meta-analysis was conducted to explore the effect of fish intake on colon and rectal cancer, respectively. We noted that the protective effect of fish consumption is more prominent in rectal cancer than that in colon cancer, which might be accounted for by the different characteristics between colon and rectal cancer. One possible reasoning for this difference may be because colon cancers are generally molecularly heterogeneous, whereas rectal cancers mostly arise via a single neoplastic pathway.
      • Li M.
      • Li J.Y.
      • Zhao A.L.
      • et al.
      Colorectal cancer or colon and rectal cancer? Clinicopathological comparison between colonic and rectal carcinomas.
      • Kalady M.F.
      • Sanchez J.A.
      • Manilich E.
      • et al.
      Divergent oncogenic changes influence survival differences between colon and rectal adenocarcinomas.
      Despite this, further exploration is needed to elucidate the exact mechanism at play. Overall, there is statistically significant heterogeneity among the 41 studies on fish consumption and colorectal cancer risk. The summary ORs among all studies did show a significant association between fish consumption and the risk of colorectal cancer. Of note, no statistically significant funnel plot asymmetry was seen when the effects of fish intake on risk of colorectal cancer were assessed via the Egger method.

      Study Limitations

      Like all meta-analyses, this meta-analysis has some potential limitations resulting from the availability, quality, and heterogeneity of the published data. There are several limitations that should be considered when interpreting our results.
      First, the methods and units of measuring fish intake varied across studies. For example, in some studies the definitive volume of fish consumption was not clearly defined, and in these cases only the lowest and highest categories were reported. Because statistical tests showed heterogeneity among some studies, we used the random-effects model, which considers both within- and between-study variation for the pooled RR estimates and the dose-response analyses.
      • Berlin J.A.
      • Longnecker M.P.
      • Greenland S.
      Meta-analysis of epidemiologic dose-response data.
      Second, we included only those studies that were published in English. This is mainly because it is difficult for the authors to interpret all the data that are available in different languages (eg, Japanese
      • Hirayama T.
      A large scale cohort study on the effect of life styles on the risk of cancer by each site.
      and Romanian
      • Fira-Mladinescu C.
      • Fira-Mladinescu O.
      • Doroftei S.
      • et al.
      [Food intake and colorectal cancers; an ecological study in Romania].
      ). Only studies with the full text published in English were included in this meta-analysis.
      Third, although we set up “fresh fish” as one of the including criteria in this meta-analysis, in an attempt to avoid such confounding factors as “fish oil,” “salted fish,” or “fried fish,” we cannot determine the exact kind of fish or the manner in which fish was prepared in our study. This represents a limitation because a recent article showed that cooking temperature might affect the risk of colorectal cancer.
      • Ngoan le T.
      • Thu N.T.
      • Lua N.T.
      • et al.
      Cooking temperature, heat-generated carcinogens, and the risk of stomach and colorectal cancers.
      Fourth, our study is limited because of the small sample size. Only 41 studies met our criteria to examine the association between fish and risk of colorectal cancer. Moreover, further sensitivity analysis restriction led to loss of statistical significance for pooled RRs, even though the risk estimates changed only slightly. Because of the small sample size, we had limited power to conclusively reject the null hypothesis of no publication bias. To account for this, we set statistical significance for publication bias at P < .1, and there was no publication bias visually found or via Egger's test. Because of these limitations, the results of the meta-analysis must be interpreted with caution.

      Conclusions

      The present meta-analysis suggests that fish consumption has preventive effects on colorectal cancer. Because of the relatively large health impact and burden from colorectal cancer, the findings demonstrated should be further investigated and confirmed in the setting of a large randomized clinical trial.
      The authors thank Jeremy Allen Meier for editing the English.

      Appendix

      Tabled 1
      MOOSE Statement
      ContentY/NPage(s)
      Problem definitionY2-4
      Hypothesis statementY2, 4
      Description of study outcome(s)Y2
      Type of exposure or intervention usedY2
      Type of study designs usedY2
      Study populationY2
      Qualifications of searchers (eg, librarians and investigators)Y2, 4, 5
      Search strategy, including time period included in the synthesis and keywordsY2, 5
      Effort to include all available studies, including contact with authorsY5
      Databases and registries searchedY5-6
      Search software used, name and version, including special features used (eg, explosion)Y2, 5
      Use of hand searching (eg, reference lists of obtained articles)N/
      List of citations located and those excluded, including justificationY8-11
      Method of addressing articles published in languages other than EnglishY14
      Method of handling abstracts and unpublished studiesN/
      Description of any contact with authorsY15
      Description of relevance or appropriateness of studies assembled for assessing the hypothesis to be testedY4-6
      Rationale for the selection and coding of data (eg, sound clinical principles or convenience)Y4-6
      Documentation of how data were classified and coded (eg, multiple raters, blinding, and interrater reliability)Y4-6
      Assessment of confounding (eg, comparability of cases and controls in studies where appropriate)Y2-6
      Assessment of study quality, including blinding of quality assessors; stratification or regression on possible predictors of study resultsY5-6
      Assessment of heterogeneityY6
      Description of statistical methods (eg, complete description of fixed or random-effects models, justification of whether the chosen models account for predictors of study results, dose-response models, or cumulative meta-analysis) in sufficient detail to be replicatedY7-11
      Provision of appropriate tables and graphicsY7-11
      Graphic summarizing individual study estimates and overall estimateY7
      Table giving descriptive information for each study includedY7-11
      Results of sensitivity testing (eg, subgroup analysis)Y7-11
      Indication of statistical uncertainty of findingsY7-11
      Quantitative assessment of bias (eg, publication bias)Y11-12
      Justification for exclusion (eg, exclusion of non-English language citations)Y14
      Assessment of quality of included studiesY7-12
      Consideration of alternative explanations for observed resultsY12-15
      Generalization of the conclusions (ie, appropriate for the data presented and within the domain of the literature review)Y15
      Guidelines for future researchY15
      Disclosure of funding sourceY15

      Supplementary data

      Figure thumbnail grsu1
      Supplemental Figure 1Consumption of fish in association with colorectal cancer in case-control and prospective cohort studies. A, Consumption of fish in association with colorectal cancer risk in case-control studies, with a combined risk estimate of 0.83 (95% CI, 0.72-0.95; Q=89.69, P<.001, I2 = 68.8%). B, Consumption of fish in association with colorectal cancer risk in prospective cohort studies, with a combined risk estimate of 0.93 (95% CI, 0.86-1.01; Q=33.49, P=.22, I2 = 16.4%). Both analyses were conducted using the random-effects model. CI=confidence interval; OR=odds ratio.
      Figure thumbnail grsu2
      Supplemental Figure 2Consumption of fish in association with colorectal cancer in hospital-based and population-based case-control studies. A, Consumption of fish in association with colorectal cancer risk in hospital-based case-control studies, with a combined risk estimate of 0.70 (95% CI, 0.59-0.84; Q=33.91, P=.001, I2 = 61.70%). B, Consumption of fish in association with colorectal cancer risk in population-based case-control studies, with a combined risk estimate of 0.96 (95% CI, 0.81-1.13; Q=31.65, P=.004, I2 = 55.8%). Both analyses were conducted using the random-effects model. CI=confidence interval; OR=odds ratio.
      Figure thumbnail grsu3
      Supplemental Figure 3Consumption of fish in association with colon or rectal cancer, respectively. A, Consumption of fish in association with colon cancer risk, with a combined risk estimate of 0.96 (95% CI, 0.81-1.14; Q=53.53, P<.001, I2 = 68.20%). B, Consumption of fish in association with rectal cancer risk, with a combined risk estimate of 0.79 (95% CI, 0.65-0.97; Q=18.89, P=.03, I2 = 52.30%). Both analyses were conducted using the random-effects model. CI=confidence interval; OR=odds ratio.
      Figure thumbnail grsu4
      Supplemental Figure 4Consumption of fish in association with colorectal cancer incidence and mortality. A, Consumption of fish in association with colorectal cancer incidence, with a combined risk estimate of 0.86 (95% CI, 0.79-0.95; Q=125.03, P<.001, I2 = 59.20%). B, Consumption of fish not significantly in association with colorectal cancer mortality, with a combined risk estimate of 1.01 (95% CI, 0.82-1.26); Q=33.49, P=.22, I2 = 16.4%). Both analyses were conducted using the random-effects model. CI=confidence interval; OR=odds ratio.

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