92006082299Cancer Epidemiol Biomarkers PrevCancer Epidemiol. Biomarkers Prev.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology1055-99651538-775527811119533639710.1158/1055-9965.EPI-16-0496NIHMS824603ArticleAlcohol Consumption and the Risk of Colorectal Cancer for Mismatch Repair Gene Mutation CarriersDashtiS. Ghazaleh1BuchananDaniel D.12JayasekaraHarindra13OuakrimDriss Ait1ClendenningMark2RostyChristophe24WinshipIngrid M.56MacraeFinlay A.567GilesGraham G.13ParrySusan8CaseyGraham9HaileRobert W.10GallingerSteven11Le MarchandLoïc12ThibodeauStephen N.13LindorNoralane M.14NewcombPolly A.1516PotterJohn D.151617BaronJohn A.18HopperJohn L.119JenkinsMark A.1WinAung Ko1Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, AustraliaColorectal Oncogenomics Group, Genetic Epidemiology Laboratory, Department of Pathology, The University of Melbourne, Parkville, Victoria, AustraliaCancer Epidemiology Centre, Cancer Council Victoria, Melbourne, Victoria, AustraliaSchool of Medicine, University of Queensland, Herston, Queensland, AustraliaDepartment of Medicine, Royal Melbourne Hospital, The University of Melbourne, Parkville, Victoria, AustraliaGenetic Medicine and Family Cancer Clinic, Royal Melbourne Hospital, Parkville, Victoria, AustraliaColorectal Medicine and Genetics, The Royal Melbourne Hospital, Parkville, Victoria, AustraliaNew Zealand Familial Gastrointestinal Cancer Service, Auckland Hospital, Auckland, New ZealandKeck School of Medicine, Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, California, USADepartment of Medicine, Division of Oncology, Stanford Cancer Institute, Stanford University, California, USALunenfeld Tanenbaum Research Institute, Mount Sinai Hospital, University of Toronto, Toronto, Ontario, CanadaEpidemiology Program, University of Hawaii Cancer Center, Honolulu, Hawaii, USAMolecular Genetics Laboratory, Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota, USADepartment of Health Science Research, Mayo Clinic Arizona, Scottsdale, Arizona, USAPublic Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USASchool of Public Health, University of Washington, Seattle, Washington, USACentre for Public Health Research, Massey University, Wellington, New ZealandDepartment of Medicine, University of North Carolina, Chapel Hill, North Carolina, USADepartment of Epidemiology and Institute of Health and Environment, School of Public Health, Seoul National University, Seoul, KoreaCorrespondence to: Aung Ko Win, MBBS, MPH, PhD, Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, Level 3, 207 Bouverie Street, The University of Melbourne VIC 3010, Australia, Phone: +61 3 9035 8238, Fax: +61 3 9349 5815, awin@unimelb.edu.au2510201603112016320170132018263366375Background
People with germline mutation in one of the DNA mismatch repair (MMR) genes have increased colorectal cancer risk. For these high-risk people, study findings of the relationship between alcohol consumption and colorectal cancer risk have been inconclusive.
Methods
1,925 MMR gene mutations carriers recruited into the Colon Cancer Family Registry who had completed a questionnaire on lifestyle factors were included. Weighted Cox proportional hazard regression models were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for the association between alcohol consumption and colorectal cancer.
Results
Colorectal cancer was diagnosed in 769 carriers (40%) at a mean (standard deviation) age of 42.6 (10.3) years. Compared with abstention, ethanol consumption from any alcoholic beverage up to 14 grams/day and >28 grams/day were associated with increased colorectal cancer risk (HR, 1.50; 95%CI, 1.09–2.07 and 1.69; 95%CI, 1.07–2.65 respectively; P-trend=0.05), and colon cancer risk (HR, 1.78; 95%CI, 1.27–2.49 and 1.94; 95%CI, 1.19–3.18 respectively; P-trend=0.02). However, there was no clear evidence for an association with rectal cancer risk. Also, there was no evidence for associations between consumption of individual alcoholic beverage types (beer, wine, spirits) and colorectal, colon, or rectal cancer risk.
Conclusion
Our data suggests that alcohol consumption, particularly more than 28 grams/day of ethanol (~2 standard drinks of alcohol in the US), is associated with increased colorectal cancer risk for MMR gene mutation carriers.
Impact
Although these data suggested that alcohol consumption in MMR carriers was associated with increased colorectal cancer risk, there was no evidence of a dose-response, and not all types of alcohol consumption were associated with increased risk.
Lynch syndrome, previously known as hereditary non-polyposis colorectal cancer (HNPCC) (1), is an autosomal dominant disorder of cancer predisposition caused by heterozygous germline mutations in the DNA mismatch repair (MMR) genes, MLH1, MSH2, MSH6, and PMS2 or a deletion in EPCAM (2). Approximately 1 in 300 to 370 people in the general population carry a mutation in an MMR gene (3, 4). MMR gene mutation carriers are at increased risk of colorectal cancer, with an estimated cumulative risk to age 70 years between 40% and 70% depending on sex and mutated MMR gene (5–9). Approximately 2–4% of all colorectal cancers (10, 11) and 10–15% of colorectal cancers diagnosed before age 50 years (12, 13) can be attributed to Lynch syndrome. Personal and lifestyle factors could modify cancer risk for MMR gene mutation carriers (14). Identifying potentially protective or harmful risk factors for this high-risk group could assist with reducing their cancer risk, as well as understanding carcinogenesis.
For the general population, evidence from systematic reviews and meta-analyses supports a positive association between alcohol consumption and colorectal cancer risk, which is likely to be most evident at moderate to high levels of alcohol consumption (15–18). There is little evidence that this increased risk differs by alcoholic beverage types, sex, or the site of the colorectum (16, 18).
For MMR gene mutation carriers, only a few studies have investigated the association between alcohol consumption and colorectal cancer risk and their findings have been inconclusive (19–21). All these studies had small sample sizes, different selection criteria for participants (confirmed mutation carriers, those who met clinical criteria for HNPCC), different outcome measures (colorectal cancer, polyps, or both), and none investigated the association between lifetime alcohol consumption and colorectal cancer risk.
In this study, we estimated the associations between lifetime alcohol consumption and colorectal cancer risk for MMR gene mutation carriers using a large dataset from the Colon Cancer Family Registry. We also estimated the associations separately for different beverage types, for colon and rectal cancers, and for men and women.
MATERIALS AND METHODSStudy Sample
This study involved carriers of heterozygous germline pathogenic mutations in MMR genes who had been recruited by the Colon Cancer Family Registry. Detailed descriptions of study design and recruitment have been published (22) and are available at http://coloncfr.org (22). Between 1997 and 2012, the Colon Cancer Family Registry recruited and interviewed probands who were either recently diagnosed with colorectal cancer that was reported to state or regional population cancer registries in the United States (Washington, California, Arizona, Minnesota, Colorado, New Hampshire, North Carolina, and Hawaii), Australia (Victoria), and Canada (Ontario); or were from multiple-case families referred to family-cancer clinics in the United States (Mayo Clinic, Rochester, Minnesota; and Cleveland Clinic, Cleveland, Ohio), Australia (Melbourne, Adelaide, Perth, Brisbane, and Sydney), Canada (Ontario), and New Zealand (Auckland). Permission was obtained from probands to contact their affected and unaffected relatives and seek their enrolment in the Colon Cancer Family Registry. For families recruited via population-based registries, first-degree relatives of probands were recruited by all centers. Recruitment was extended to more distant relatives by some centers. For families recruited via family-cancer clinics, attempts were made to recruit also second-degree relatives of affected individuals (details provided by Newcomb et al. (22)). Informed consent was obtained from all participants and the study protocol was approved by research ethics review board at each recruitment center.
Data Collection
At the time of baseline recruitment, standardized questionnaires were used to collect self-reported information on demographics, lifestyle factors including alcohol consumption, personal and family history of cancer, cancer-screening history, and history of polyps, polypectomy, and other surgeries from all participants via personal interviews, telephone interviews, or mailed questionnaires. The questionnaires used by the Colon Cancer Family Registry centers are available at http://coloncfr.org/questionnaires. Pathology reports, medical records, cancer registry reports, and death certificates were consulted, when possible, to confirm reported cancer diagnoses and age at diagnosis. Attempts were made to obtain blood samples from all participants and tumor tissue samples from all colorectal-cancer-affected participants.
MMR Gene Mutation Testing
Testing for germline mutations in MLH1, MSH2, MSH6, and PMS2 was performed for all population-based probands who had a colorectal tumor that showed impaired MMR function, as evidenced by tumor microsatellite instability (MSI) or absence of MMR-protein expression in immunohistochemical analysis. Testing was also performed for the colorectal-cancer-affected participants from clinic-based families regardless of tumor MSI or MMR-protein expression status. Sanger sequencing or denaturing high performance liquid chromatography, followed by confirmatory DNA sequencing, was performed to screen for mutations in the MLH1, MSH2, and MSH6 genes. Large duplication and deletion mutations were detected by Multiplex Ligation Dependent Probe Amplification (MLPA) according to the manufacturer’s instructions (MRC Holland, Amsterdam, the Netherlands) (22–24). PMS2 mutation testing involved a modified protocol from Senter et al (7), in which exons 1 to 5, 9, and 11 to 15 were amplified with 3 long-range polymerase chain reactions (PCRs) followed by nested exon-specific PCR and sequencing. The remaining exons (7, 8, and 10) were amplified and sequenced directly from genomic DNA. Large-scale deletions in PMS2 were detected using the P008-A1 MLPHA kit (MRC Holland, Amsterdam, the Netherlands) (25). Relatives of probands with a pathogenic MMR germline mutation (26) who provided a blood sample were tested for the specific mutation identified in the proband.
Definitions of Exposure and Outcome
The primary outcome was self-reported diagnosis of colorectal cancer. The primary exposure was grams of ethanol consumption per day from alcoholic beverages from age 20 years to age at colorectal cancer diagnosis or age at censoring.
At baseline, participants were asked to report the number of 12-oz servings of beer or alcoholic cider (included as beer in the remainder of this paper), 4-oz servings of wine or 1-oz servings of sake (included as wine), and 1-oz servings of spirits that they had consumed daily or weekly in their 20s, 30s and 40s, and 50s and older, as well as the number of years they had consumed the alcoholic beverages at least once a week for 6 months or longer during each of these periods.
Grams of ethanol in alcoholic beverages (14 grams in 12-oz serving of beer, 11.2 grams in 4-oz serving of wine, 3.5 grams in 1-oz serving of sake, and 9.3 grams in 1-oz serving of spirits) were calculated based on average alcohol content in each drink (5% in beer, 12% in wine, 15% in sake, and 40% in spirits), density of ethanol (0.79 grams/ml), and converting US/British Imperial measurements of volume to metric (1 oz. equivalent to 29.57 ml).
Grams of ethanol consumption per day from alcoholic beverages when drinking beer, wine, and spirits were calculated based on self-reported number of alcoholic beverages consumed and years of alcohol consumption from age 20 years to age at colorectal cancer diagnosis or age at censoring. Abstainers were defined as carriers who had not consumed alcohol for at least once a week for 6 months or longer throughout this period.
Statistical Analysis
Cox proportional regression analyses, with age as the time scale, were used to estimate the association between alcohol consumption and the risk of colorectal cancer for MMR gene mutation carriers. Time at risk started at age 20 years and ended at age of first colorectal cancer diagnosis, any other cancer diagnosis, polypectomy, or age at baseline interview, whichever occurred first. Observation time ended at age of diagnosis of any other cancer, because subsequent cancer treatment and surveillance could have altered later colorectal cancer risk and MMR gene mutations carriers might have changed their behavior following cancer diagnosis.
Because colorectal cancer cases from multiple-cancer families and cases with early-onset colorectal cancer were preferentially tested for MMR gene mutations, selection of carriers was not random with respect to their disease status. This non-random ascertainment was adjusted for in the analysis by applying probability weights to carriers based on the weighted cohort approach developed by Antoniou et al. (27). Age-specific incidences of colorectal cancer for MMR gene mutation carriers (28) were used to calculate statistical weights for colorectal-cancer-affected and -unaffected carriers for each age-stratum so the proportion of affected carriers in each age-stratum was equal to the proportion of affected carriers in the general population.
Variables that were considered as potential confounders are listed in Table 1. When applicable, all variables, including the alcohol consumption variables, were treated as time-varying covariates. We were not able to generate time-varying variables for consumption of aspirin or ibuprofen, and multivitamin, calcium, and folic acid supplements because we did not have information on carriers’ age at first exposure to regular consumption of these medications and supplements. For investigating the associations between consumption of specific types of alcoholic beverages with colorectal cancer risk, after confirming that these variables were not strongly correlated (all correlation coefficients were ≤0.25) and running collinearity diagnostic tests, consumption of the other two sources of ethanol were included as potential confounders in the multivariable models. The proportional hazards assumption was tested with the Schoenfeld and scaled Schoenfeld residuals (29). The confounding variables that did not meet the proportional hazards assumption were stratified for in the final models. The overall model fit was assessed using Cox-Snell residuals as the time variable and plotting them against the Nelson-Aalen cumulative hazard function (30). For continuous variables, deviation from linearity was tested using the likelihood ratio test in comparison between models with the original variable and models that included the original and quadric transformation of the variable. To test for interaction, the difference in the log-likelihood ratio was assessed after adding a cross-product term between each exposure variable and the potential effect modifiers identified a priori. The numbers of missing values for all variables are reported in Tables 1 to 2. All univariable analyses were complete case analyses. In the multivariable models, all confounder variables were fitted as categorical variables with their missing values (all <8%) coded as an additional category.
We estimated associations by each colorectal cancer site, sex and cancer site, smoking status, body mass index (BMI) at age 20 years, and history of having received sigmoidoscopy or colonoscopy. The following additional analyses were also conducted: analyses with colorectal polyp or cancer as the outcome; and analyses restricted to carriers who received a colorectal cancer diagnosis or were censored within 5 years before interview to reduce survival bias. To take into account the potential correlation in risk between family-members, we used the Huber-White robust variance estimation by clustering on family membership (31, 32). All statistical tests were two-sided. All statistical analyses were performed using STATA statistical software, version 13.0 (Stata Corp LP, College Station, Texas, USA).
RESULTS
A total of 2,031 MMR gene mutation carriers were identified from the Colon Cancer Family Registry. Of these, the 53 (2.6%) who were younger than 20 years old at the time of colorectal cancer diagnosis (n=11) or censoring (n=42) were excluded. We also excluded 16 colorectal cancer-affected and 36 unaffected carriers for whom no data on alcohol consumption were available, and one carrier whose reported average ethanol consumption from alcoholic beverages was more than 200 grams per day. Excluded carriers had similar characteristics compared to those who were included in this study (details not shown).
The final sample comprised 1,925 MMR gene mutation carriers (920 were recruited as colorectal affected cases and 1005 as unaffected) from 761 families. Of these, 1,077 (56%) were female; 1,425 (74%) were recruited through family-cancer clinics; and 714 carried a mutation in MLH1, 898 in MSH2, 215 in MSH6, and 98 in PMS2. Time at risk ended at age at colorectal cancer diagnosis for 769 carriers (643 located in the colon, 116 in the rectum, and 10 in both the colon and rectum), polypectomy for 338, other cancer diagnosis for 224, and interview for 594. The time interval (standard deviation, SD) between age at colorectal cancer diagnosis and age at interview for affected carriers was 9.1 (9.8) years, and between censored age and age at interview for unaffected carriers was 5.1 (8.8) years (P=0.20). Colorectal cancer incidence from age 20 was 1.8 (95% confidence interval (CI), 1.68 – 1.93) per 100 person-years and mean (SD) age at diagnosis was 42.6 (10.3) years. Colorectal cancer diagnosis was confirmed for 704 affected carriers (92%) by pathology reviews or reports, cancer registries, or hospital records.
Characteristics of MMR gene mutation carriers included in the present study are summarized in Table 1. On average, men had higher consumption of ethanol from any alcoholic beverage than women (mean (SD) in unaffected carriers: 15.9 (21.1) vs. 4.2 (9.6) grams per day) as well as from beer and spirits. Average ethanol consumption per day from any alcoholic beverage and from beer were higher in colorectal cancer-affected carriers compared with unaffected carriers (12.9 (21.4) vs. 8.6 (16.0) grams per day and 8.7 (19.3) vs. 4.9 (11.1) grams per day, respectively) (Table 2).
Compared with abstention, consumption of ethanol from any alcoholic beverage both up to 14 grams/day and >28 grams/day were associated with an increased risk of colorectal cancer (HR, 1.50; 95% CI, 1.09 – 2.07 and 1.69; 95% CI, 1.07 – 2.65 respectively). There was evidence for a linear dose-dependent relationship between ethanol intake from any alcoholic beverage and colorectal cancer risk in the univariable model (HR per 14 grams/day, 1.07; 95% CI, 1.01 – 1.14), but not in the multivariable model (HR per 14 grams/day, 1.02; 95% CI, 0.94 – 1.09). Similarly, there were associations between beer and spirit consumption and colorectal cancer risk in the univariable models, but no clear association between the consumption of any alcohol type (beer, wine or spirits) and colorectal cancer risk in the multivariable models (Table 3).
An increased risk of colon, but not rectal, cancer was associated with ethanol consumption from any alcoholic beverage of up to 14 grams/day (HR, 1.78; 95% CI, 1.27 – 2.49) and >28 grams/day (HR, 1.94; 95% CI, 1.19 – 3.18) in multivariable models (Table 4). Similar results were seen in analyses when the outcome was defined as colorectal polyp or cancer (HR for ethanol >28 grams/day vs. abstention, 1.46; 95% CI, 1.15 – 1.86) (Supplementary Table 1), and in analyses restricted to carriers who were diagnosed with colorectal cancer or censored within 5 years before interview (HR for ethanol >28 grams/day vs. abstention, 1.29; 95% CI, 0.65 – 2.53) (Supplementary Table 2).
There was no evidence for an interaction between alcohol consumption and sex (P=0.78; Figure 1), cigarette smoking status (P=0.61), BMI at age 20 (P=0.88), history of having received sigmoidoscopy or colonoscopy (P=0.43) (Supplementary Table 3), the specific mutated MMR gene (P=0.99), country of recruitment (P=0.99), ascertainment method (P=0.11), or folic acid supplement intake (P=0.37) (Supplementary Table 4). There was no evidence of non-linearity for continuous variables, or of violation of the proportional hazards assumption by any of the main alcohol variables in any of the final models. The unweighted and weighted analyses yielded similar results, although the weighted analyses gave larger standard errors and wider confidence intervals (details not shown).
DISCUSSION
We found that alcohol consumption particularly more than 28 grams/day of ethanol (~2 standard drinks of alcohol in the US) was associated with an increased risk of colorectal cancer for MMR gene mutation carriers. The direction and strength of associations that we estimated for carriers were similar to those reported by a meta-analysis of 7 studies on associations between long-term alcohol consumption and colorectal cancer (relative risk for highest vs. lowest category, 1.49; 95% CI, 1.27 – 1.74) (15), and meta-analyses of associations between current alcohol consumption and colorectal cancer risk (17, 18, 33), estimated for the general population. For the general population, alcohol consumption has been reported to increase the risk of both colon and rectal cancer (16, 18). In this study, however, we did not find evidence for a positive association between alcohol consumption and rectal cancer. While our overall results were not contrary to findings for the general population (16) (95% CIs were compatible), the small number of rectal cancer cases with available data on alcohol consumption (n=104) could possibly explain the lack of evidence for an association between alcohol consumption and rectal cancer in our study (in MMR gene mutation carriers, proximal colon is the predominant site for colorectal cancers (34)). Similarly, our study provided no clear evidence for an association between individual beverage types and colorectal, colon, or cancer risk and in separate analyses for men and women. This might be due to limited statistical power given the limits of confidence intervals for estimates from these sub-analyses. The reason for absence of a clear dose-dependent relationship between alcohol consumption and colorectal cancer risk in our study is not clear. However, in part, it might be because the majority of this cohort (n=831) were light alcohol drinkers (0 to 14 grams per day), a limited number of carriers (n=188; 16 women and 172 men) were in our highest category of alcohol consumption (more than 28 grams per day), and our study was under powered to assess dose-dependent relationship, particularly at high levels of alcohol consumption.
A few previous studies have investigated the association between alcohol consumption and the risk of colorectal cancer (19), colorectal polyp or cancer (20), and colorectal adenoma (21), for MMR gene mutation carriers. In a retrospective cohort study of 271 MMR gene mutation carriers, Watson et al. reported no evidence for an association between alcohol use and colorectal cancer risk (P>0.4; estimates not reported) (19). In a Dutch case-control study of suspected and confirmed MMR gene mutation carriers (145 cases with colorectal cancer (n=36) or polyps (n=119) vs. 103 controls), Diergaarde et al. did not find evidence for an association between alcohol consumption and the risk of colorectal polyp or cancer (odds ratio ≥12.8 grams/day vs. ≤2.6 grams/day, 1.0; 95% CI 0.5 – 2.0) (20). However, as here, those authors reported greater alcohol consumption by cases than controls (mean (SD) 14.0 (15.2) vs. 9.8 (10.9)) (20). In a prospective cohort study of 386 MMR gene mutation carriers (58 diagnosed with colorectal adenoma over a median follow-up of 10 months), Winkels et al. reported an HR of 1.56 (95% CI, 0.71 – 3.43) for the association between highest vs. lowest tertiles of alcohol consumption and the risk of colorectal adenoma. This is similar to our estimate from analysis in which the outcome was defined as either colorectal polyp or cancer (HR >28 grams/day vs. abstention, 1.60; 95% CI, 1.16 – 2.20) (21). The smaller sample sizes of those studies relative to ours could explain their lack of evidence for associations between alcohol consumption and the risk of colorectal polyp or cancer.
The mechanisms of alcohol-related carcinogenesis specifically for MMR gene mutation carriers are not known. For the general population, it has been suggested that acetaldehyde, a metabolite of ethanol found in high concentrations in the colon following alcohol consumption, has a carcinogenic role. Acetaldehyde affects DNA synthesis and repair, alters the structure and function of glutathione (an anti-oxidative peptide), and increases mucosal proliferation in the colon (35). There is also evidence that ethanol could have carcinogenic effects by altering methyl group transfer (35).
To the best of our knowledge, this is the largest study to date examining the association between alcohol consumption and colorectal cancer risk for MMR gene mutation carriers. Data for this study came from the Colon Cancer Family Registry, which used standardized and uniform questionnaires and conducted standardized genetic screening for MMR gene mutations (22). To handle the fact that selection of carriers was on the basis of their disease status, we used a weighted cohort analyses to produce unbiased estimates (27).
One of the limitations of our study is potential for imprecision in estimates, and attenuation toward the null due to non-differential errors in the measurement of alcohol consumption and other variables because they were based on self-reported questionnaires. In addition, the assumptions that underpinned our calculation of average alcohol consumption between the age of 20 years and age at colorectal cancer diagnosis or censored age might have overestimated alcohol consumption for both colorectal affected and unaffected MMR gene mutation carriers. Because affected carriers were interviewed after having been diagnosed with colorectal cancer, disease status might have influenced their recall of exposures, introducing recall bias. Additionally, there may have been residual confounding by factors that we were not able to take into account in our analyses. BMI is a known risk factor for colorectal cancer (36) and might have confounded the association between alcohol consumption and colorectal cancer. We were not able to adjust for recent BMI because this variable was available for only 50% of carriers in this study. Because we did not have data on age at first regular consumption of aspirin or ibuprofen, or of multivitamin, calcium, and folic acid supplements, we were not able to treat them as time-varying variables in our analyses. When included as time-fixed variables in the models, the strength and direction of estimated alcohol associations did not change. Finally, survival bias might have influenced our analysis as affected carriers with better survival were more likely to have been recruited into the Colon Cancer Family Registry. However, a supplementary analysis limited to carriers who were censored or diagnosed with colorectal cancer within 5 years before the interview gave similar results to our main analysis.
For MMR gene mutation carriers, alcohol consumption, particularly more than 28 grams per day of ethanol, was associated with an increased risk of colorectal cancer. However, there was no clear evidence for a dose-dependent relationship or increased risks associated with individual beverage types. These findings need further confirmation, particularly using large prospective cohort studies.
Supplementary Material
This work was supported by grant UM1 CA167551 from the National Cancer Institute and through cooperative agreements with the following Colon Cancer Family Registry centres: Australasian Colorectal Cancer Family Registry (U01 CA074778 and U01/U24 CA097735); Mayo Clinic Cooperative Family Registry for Colon Cancer Studies (U01/U24 CA074800); Ontario Familial Colorectal Cancer Registry (U01/U24 CA074783); Seattle Colorectal Cancer Family Registry (U01/U24 CA074794); University of Hawaii Colorectal Cancer Family Registry (U01/U24 CA074806); and USC Consortium Colorectal Cancer Family Registry U01/U24 CA074799).
Seattle CCFR research was also supported by the Cancer Surveillance System of the Fred Hutchinson Cancer Research Center, which was funded by Control Nos. N01-CN-67009 (1996–2003) and N01-PC-35142 (2003–2010) and Contract No. HHSN2612013000121 (2010–2017) from the Surveillance, Epidemiology and End Results (SEER) Program of the National Cancer Institute with additional support from the Fred Hutchinson Cancer Research Center.
The collection of cancer incidence data for the State of Hawaii used in this study was supported by the Hawaii Department of Health as part of the state-wide cancer reporting program mandated by Hawaii Revised Statutes; the National Cancer Institute’s Surveillance, Epidemiology and End Results Program (SEER) under Control Nos. N01-PC-67001 (1996–2003) and N01-PC-35137 (2003–2010) and Contract Nos. HHSN26120100037C (2010–2013) and HHSN261201300009I (2010-current) awarded to the University of Hawaii. The ideas and opinions expressed herein are those of the author(s) and endorsement by the State of Hawaii, Department of Health, the National Cancer Institute, SEER Program or their Contractors and Subcontractors is not intended nor should be inferred.
The collection of cancer incidence data used in this study was supported by the California Department of Public Health as part of the state-wide cancer reporting program mandated by California Health and Safety Code Section 103885; the National Cancer Institute’s Surveillance, Epidemiology and End Results Program under contract HHSN261201000140C awarded to the Cancer Prevention Institute of California, contract HHSN261201000035C awarded to the University of Southern California, and contract HHSN261201000034C awarded to the Public Health Institute; and the Centers for Disease Control and Prevention’s National Program of Cancer Registries, under agreement U58DP003862-01 awarded to the California Department of Public Health. The ideas and opinions expressed herein are those of the author(s) and endorsement by the State of California, Department of Public Health the National Cancer Institute, and the Centers for Disease Control and Prevention or their Contractors and Subcontractors is not intended nor should be inferred.
This study was also supported by Centre for Research Excellence grant APP1042021 and Program Grant APP1074383 from the National Health and Medical Research Council (NHMRC), Australia. AKW is an NHMRC Early Career Fellow. MAJ is an NHMRC Senior Research Fellow. JLH is a NHMRC Senior Principal Research Fellow. DDB is a University of Melbourne Research at Melbourne Accelerator Program (R@MAP) Senior Research Fellow. CR is a Jeremy Jass Pathology Fellow.
The authors thank all study participants of the Colon Cancer Family Registry and staff for their contributions to this project.
DISCLAIMER
The content of this manuscript does not necessarily reflect the views or policies of the National Cancer Institute or any of the collaborating centers in the Colon Cancer Family Registry (CCFR), nor does mention of trade names, commercial products, or organizations imply endorsement by the US Government or the CCFR. Authors had full responsibility for the design of the study, the collection of the data, the analysis and interpretation of the data, the decision to submit the manuscript for publication, and the writing of the manuscript.
DISCLOSURE
The authors have no conflict of interest to declare with respect to this manuscript.
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A review of human carcinogensIARC Monographs On The Evaluation Of Carcinogenic Risks To Humans / World Health Organization, International Agency For Research On Cancer20121001538FedirkoVTramacereIBagnardiVRotaMScottiLIslamiFAlcohol drinking and colorectal cancer risk: an overall and dose-response meta-analysis of published studiesAnn Oncol20112219587221307158ChoESmith-WarnerSARitzJvan den BrandtPAColditzGAFolsomARAlcohol intake and colorectal cancer: a pooled analysis of 8 cohort studiesAnn Intern Med20041406031315096331WatsonPAshwathnarayanRLynchHTRoyHKTobacco Use and Increased Colorectal Cancer Risk in Patients With Hereditary Nonpolyposis Colorectal Cancer (Lynch Syndrome)Arch Intern Med200416424293115596632DiergaardeBBraamHVasenHFNagengastFMMuijenGNPvKokFJEnvironmental Factors and Colorectal Tumor Risk in Individuals With Hereditary Nonpolyposis Colorectal CancerClin Gastroenterol Hepatol2007573642.e117544999WinkelsRMBotmaAVan DuijnhovenFJNagengastFMKleibeukerJHVasenHFSmoking increases the risk for colorectal adenomas in patients with Lynch syndromeGastroenterology2012142241722062356NewcombPABaronJCotterchioMGallingerSGroveJHaileRColon Cancer Family Registry: an international resource for studies of the genetic epidemiology of colon cancerCancer Epidemiol Biomarkers Prev20071623314317982118RumillaKSchowalterKVLindorNMThomasBCMensinkKAGallingerSFrequency of deletions of EPCAM (TACSTD1) in MSH2-associated Lynch syndrome casesJ Mol Diagn20111393921227399SoutheyMCJenkinsMAMeadLWhittyJTrivettMTesorieroAAUse of molecular tumor characteristics to prioritize mismatch repair gene testing in early-onset colorectal cancerJ Clin Oncol20052365243216116158RostyCClendenningMWalshMDEriksenSVSoutheyMCWinshipIMGermline mutations in PMS2 and MLH1 in individuals with solitary loss of PMS2 expression in colorectal carcinomas from the Colon Cancer Family Registry CohortBMJ Open20166e010293eWinAKLindorNMYoungJPMacraeFAYoungGPWilliamsonERisks of primary extracolonic cancers following colorectal cancer in Lynch syndromeJ Natl Cancer Inst201210413637222933731AntoniouACGoldgarDEAndrieuNChang-ClaudeJBrohetRRookusMAA weighted cohort approach for analysing factors modifying disease risks in carriers of high-risk susceptibility genesGenet Epidemiol20052911115880399PandeMLynchPMHopperJLJenkinsMAGallingerSHaileRWSmoking and Colorectal Cancer in Lynch Syndrome: Results from the Colon Cancer Family Registry and The University of Texas M.D. Anderson Cancer CenterClin Cancer Res2010161331920145170GrambschPMTherneauTMProportional Hazards Tests and Diagnostics Based on Weighted ResidualsBiometrika199451526ClevesMAAn introduction to survival analysis using StataCollege Station, TexStata Press20082nd2008RogersWHRegression standard errors in clustered samplesStata Technical Bulletin199331923WilliamsRLA Note on Robust Variance Estimation for Cluster-Correlated DataBiometrics200056645610877330MoskalANoratTFerrariPRiboliEAlcohol intake and colorectal cancer risk: a dose-response meta-analysis of published cohort studiesInt J Cancer20071206647117096321LynchHTde la ChapelleAGenetic susceptibility to non-polyposis colorectal cancerJ Med Genet1999368011810544223SeitzHKStickelFMolecular mechanisms of alcohol-mediated carcinogenesisNat Rev Cancer2007759961217646865KuipersEJGradyWMLiebermanDSeufferleinTSungJJBoelensPGColorectal cancerNat Rev Dis Primers201511506527189416
Hazard ratios for associations between alcohol consumption and the risk of colon, rectum, and colorectal cancer for DNA mismatch repair gene germline mutation carriers by sex
All models were multivariable and adjusted for country (categorical, time-fixed), education (categorical, time-fixed l), ascertainment (binary, time-fixed), at age 20 (categorical, time-fixed), diabetes status (binary, time-varying), regular physical activity (binary, time-varying), and smoking status (categorical, time-varying)
Models for women were additionally adjusted for number of live births (categorical, time-varying), and years of hormonal contraceptive use (categorical, time-varying)
Models for beer were additionally adjusted for average daily ethanol intake from wine (binary, time-varying) and from spirits (binary, time-varying)
Models for wine were additionally adjusted for average daily ethanol intake from beer (binary, time-varying) and from spirits (binary, time-varying)
Models for sake were additionally adjusted for average daily ethanol intake from beer (binary, time-varying) and from wine (binary, time-varying)
Characteristics* of DNA mismatch repair gene germline mutation carriers
No colorectal cancerN = 1,156 (60%)
Colorectal cancerN = 769 (40%)
TotalN = 1,925
Sex
Female
725 (62.7)
352 (45.8)
1,077 (56.0)
Male
431 (37.3)
417 (54.2)
848 (44.1)
Study Centers, n (%)
Australia or New Zealand
670 (58.0)
365 (47.5)
1,035 (53.8)
USA
326 (28.2)
286 (37.2)
612 (31.8)
Canada
160 (13.8)
118 (15.3)
278 (14.4)
Race
Caucasian
1,093 (94.6)
693 (90.1)
1,786 (92.8)
Other
41 (3.6)
64 (8.3)
105 (5.5)
Missing
22 (1.9)
12 (1.6)
34 (1.8)
Ascertainment method
Clinic
915 (79.2)
510 (66.3)
1,425 (74.0)
Population
241 (20.9)
259 (33.7)
500 (26.0)
Age (year),a
mean (SD)
41.9 (12.8)
42.6 (10.3)
42.2 (11.9)
median [range]
41 [20 – 85]
43 [20 – 75]
42 [20 – 85]
Year of Birth, n (%)
1914–1945
256 (22.2)
234 (30.4)
490 (25.5)
1946–1955
283 (24.5)
234 (30.4)
517 (26.9)
1956–1965
259 (22.4)
192 (25.0)
451 (23.4)
1966–1990
258 (31.0)
109 (14.2)
467 (24.3)
Education level, n (%)
Some high school or less
237 (20.5)
180 (23.4)
417 (21.7)
Completed high school/some tertiary study
387 (33.5)
259 (33.7)
646 (33.6)
Vocational/technical school
209 (18.1)
127 (16.5)
336 (17.5)
University degree
318 (27.5)
196 (25.5)
514 (26.7)
Missing
5 (0.4)
7 (0.9)
12 (0.6)
Mismatch repair gene mutated, n (%)
MLH1
381 (33.0)
333 (43.3)
714 (37.1)
MSH2
571 (49.4)
327 (42.5)
898 (46.7)
MSH6
151 (13.1)
64 (8.3)
215 (11.2)
PMS2
53 (4.6)
45 (5.9)
98 (5.1)
BMI at age 20,b n (%)
Normal
788 (68.2)
489 (63.6)
1,277 (66.3)
Overweight
183 (15.8)
149 (19.4)
332 (17.3)
Obese
46 (4.0)
42 (5.5)
88 (4.6)
Underweight
101 (8.7)
61 (7.9)
162 (8.4)
Missing
38 (3.3)
28 (3.6)
66 (3.4)
BMI 2 years before diagnosed/censored age,b,c n (%)
Normal
338 (29.2)
91 (11.8)
429 (22.3)
Overweight
224 (19.4)
114 (14.8)
338 (17.6)
Obese
113 (9.8)
60 (7.8)
173 (9.0)
Underweight
21 (1.8)
7 (0.9)
28 (1.5)
Missing
460 (39.8)
497 (64.6)
957 (49.7)
Number of received sigmoidoscopy or colonoscopy, n (%)
0
380 (32.9)
185 (24.1)
565 (29.4)
1
266 (23.0)
227 (29.5)
493 (25.6)
2
128 (11.1)
31 (4.0)
159 (8.3)
3 or more
138 (11.9)
49 (6.4)
187 (9.7)
Missing
244 (21.1)
277 (36.0)
521 (27.1)
Diabetes, n (%)
No
1,117 (96.6)
727 (94.5)
1,844 (95.8)
Yes
33 (2.9)
34 (4.4)
67 (3.5)
Missing
6 (0.5)
8 (1.0)
14 (0.7)
at the time of colorectal cancer diagnosis or age of another cancer, or polypectomy, or interview for colorectal cancer-unaffected participants (whichever came first)
Age of diagnosis of colorectal cancer for affected participants; age of diagnosis of another cancer or polypectomy or interview for colorectal cancer-unaffected participants (whichever came first)
Body mass index (BMI) calculated as weight in kilograms divided by height in meters squared. Underweight (<18.5), normal (18.5 – 24.9), overweight (25.0 – 29.9), obese (≥30)
Carriers who were diagnosed with colorectal cancer or censored more than 2 years before interview had missing for this variable
Characteristics* of DNA mismatch repair gene germline mutation carriers – continued
No colorectal cancerN = 1,156 (60%)
Colorectal cancerN = 769 (40%)
TotalN = 1,925
Aspirin and/or ibuprofen intake,a n (%)
<1 month
903 (78.1)
632 (82.2)
1,535 (79.7)
≥1 month
190 (16.4)
95 (12.4)
285 (14.8)
Missing
63 (5.5)
42 (5.5)
105 (5.5)
Multivitamin supplement intake,a n (%)
<1 month
789 (68.3)
584 (75.9)
1,373 (71.3)
≥1 month
309 (26.7)
141 (18.3)
450 (23.4)
Missing
58 (5.0)
44 (5.7)
102 (5.3)
Calcium supplement intake,a n (%)
<1 month
991 (85.7)
702 (91.3)
1,693 (88.0)
≥1 month
121 (10.5)
48 (6.2)
169 (8.8)
Missing
44 (3.8)
19 (2.5)
63 (3.3)
Folic acid supplement intake,a n (%)
<1 month
1,012 (87.5)
709 (92.2)
1,721 (89.4)
≥1 month
112 (9.7)
39 (5.1)
151 (7.8)
Missing
32 (2.8)
21 (2.7)
53 (2.8)
Cigarette smoking,b n (%)
Never
624 (54.0)
345 (44.9)
969 (50.3)
Former
262 (22.7)
160 (20.8)
422 (21.9)
Current
267 (23.1)
262 (34.1)
529 (27.5)
Missing
3 (0.3)
2 (0.3)
5 (0.3)
Regular physical activity,c n (%)
<3 months
51 (4.4)
47 (6.1)
98 (5.1)
≥3 months
1,074 (92.9)
691 (89.9)
1,765 (91.7)
Missing
31 (2.7)
31 (4.0)
62 (3.2)
Fruit and vegetable intake (servings/day) 2 years before diagnosed/censored age,d,e n (%)
<2
190 (16.4)
111 (14.4)
301 (15.6)
2.01 – 3
129 (11.2)
45 (5.9)
174 (9.0)
3.01 – 4
134 (11.6)
47 (6.1)
181 (9.4)
≥4.01
256 (22.2)
67 (8.7)
323 (16.8)
Missing
447 (38.7)
499 (64.9)
946 (49.1)
Red meat intake (servings/day 2 years before diagnosed/censored age),e,f n (%)
<0.30
211 (18.3)
88 (11.4)
299 (15.5)
0.31 – 0.60
240 (20.8)
83 (10.8)
323 (16.8)
0.61 – 0.90
102 (8.8)
45 (5.9)
147 (7.6)
≥0.91 or more
153 (13.2)
55 (7.2)
208 (10.8)
Missing
450 (38.9)
498 (64.8)
948 (49.3)
Number of live births,g n (%)
No
198 (27.3)
66 (18.8)
264 (24.5)
1
76 (10.5)
51 (14.5)
127 (11.8)
2
204 (28.1)
96 (27.3)
300 (27.9)
≥3
227 (31.3)
126 (35.8)
353 (32.8)
Missing
20 (2.8)
13 (3.7)
33(3.1)
Hormonal contraception use,g n (%)
<1 year
187 (25.8)
100 (28.4)
287 (26.7)
≥1 year
527 (72.7)
240 (68.2)
767 (71.2)
Missing
11 (1.5)
12 (3.4)
23 (2.1)
Menopause status,g n (%)
Pre-menopause
481 (66.3)
230 (65.3)
711 (66.0)
Pre-menopause
213 (29.3)
112 (31.9)
325 (30.2)
Missing
31 (4.3)
10 (2.8)
41 (3.8)
at the time of colorectal cancer diagnosis or age of another cancer, or polypectomy, or interview for colorectal cancer-unaffected participants (whichever came first)
At least twice a week
Former smokers defined as carriers who had smoked at least 1 cigarette per day for at least 3 months and had quit more than 2 years before age at colorectal cancer or censored age; current smokers defined as carriers who had smoked at least 1 cigarette per day for at least 3 months and continued within 2 years of age at colorectal cancer or censored age
Regular physical activity defined as any physical activity for at least 30 minutes per week for at least 3 months
A serving of fruit defined as 1 medium fresh fruit, or ½ cup of chopped, cooked, or canned fruit, or ¼ cup of dried fruit, or 6 ounce of fruit juice; a serving of vegetable defined as 1 cup raw leafy vegetables, or ½ cup of other vegetables, or cooked or chopped raw, 6 ounces of vegetable juice
Carriers who were diagnosed with colorectal cancer or censored more than 2 years before interview had missing for this variable
A serving of red meat defined as 2–3 ounces of red meat, or a piece of meat about the size of a deck of cards
Limited to women (725 unaffected and 352 colorectal cancer affected)
Alcohol consumption* in DNA mismatch repair gene germline mutation carriers
Women
Men
Overall
No colorectal cancer
Colorectal cancer
Total
No colorectal cancer
Colorectal cancer
Total
No colorectal cancer
Colorectal cancer
Total
N = 725 (67%)
N = 352 (33%)
N = 1,077
N = 431 (51%)
N = 417 (49%)
N = 848
N = 1,156 (60%)
N = 769 (40%)
N = 1,925
Average daily ethanol intake from any alcoholic beverage (grams/day),
mean (SD)
4.2 (9.6)
3.9 (7.7)
4.1 (9.1)
15.9 (21.1)
20.4 (25.9)
18.1 (23.7)
8.6 (16.0)
12.9 (21.4)
10.3 (18.5)
median [range]
1.1 [0–176.8]
0.6 [0–63.8]
0.9 [0–176.8]
9.2 [0–157.9]
11.9 [0–172.0]
9.9 [0–172.0]
3.1 [0–176.8]
3.5 [0–172.0]
3.2 [0–176.8]
Missing (%)
80 (11.0)
41 (11.6)
121 (11.2)
42 (9.7)
47 (11.3)
89 (10.5)
122 (10.6)
88 (11.4)
210 (10.9)
Average daily ethanol intake from beer (grams/day),
mean (SD)
1.3 (4.1)
1.2 (4.0)
1.2 (4.0)
10.9 (15.5)
15.1 (24.1)
13.0 (20.3)
4.9 (11.1)
8.7 (19.3)
6.4 (15.0)
median [range]
0 [0–43.9]
0 [0–38]
0 [0–44]
5.3 [0–93.3]
6.7 [0–168]
6 [0–168]
0 [0–93.3]
0.7 [0–168]
0 [0–168]
Missing (%)
44 (6.1)
25 (7.1)
69 (6.4)
25 (5.8)
29 (7.0)
54 (6.4)
69 (6.0)
54 (7.0)
123 (6.4)
Average daily ethanol intake from wine (grams/day),
mean (SD)
1.8 (4.4)
1.4 (3.5)
1.6 (4.1)
2.2 (5.6)
1.8 (4.3)
2.0 (5.0)
1.9 (4.9)
1.6 (4.0)
1.8 (4.5)
median [range]
0 [0–71.2]
0 [0–29.6]
0 [0–71.2]
0 [0–69.3]
0 [0–30.1]
0 [0–69.3]
0 [0–71.2]
0 [0–30.1]
0 [0–71.2]
Missing (%)
40 (7.6)
23 (6.5)
63 (5.8)
21 (4.9)
18 (4.3)
39 (4.6)
61 (5.3)
41 (5.3)
102 (5.3)
Average daily ethanol intake from spirits (grams/day),
mean (SD)
1.1 (4.6)
1.1 (2.8)
1.1 (4.1)
2.5 (6.1)
2.9 (9.3)
2.7 (7.8)
1.6 (5.3)
2.0 (7.2)
1.8 (6.1)
median [range]
0 [0–105.6]
0 [0–22.1]
0 [0–105.6]
0 [0–50.8]
0 [0–111.6]
0 [0–111.6]
0 [0–105.6]
0 [0–111.6]
0 [0–111.6]
Missing (%)
55 (5.5)
27 (7.7)
82 (7.6)
31 (7.2)
30 (7.2)
61 (7.2)
86 (7.4)
57 (7.4)
143 (7.4)
at the time of colorectal cancer diagnosis or age of another cancer, or polypectomy, or interview for colorectal cancer-unaffected participants (whichever came first)
Hazard ratios for associations between alcohol consumption and the risk of colorectal cancer for DNA mismatch repair gene germline mutation carriers
Univariable model
Multivariable modela
Multivariable modela,b
Cases
Person-years
HR (95% CI)
P value
HR (95% CI)
P value
HR (95% CI)
P value
Average daily ethanol intake from any alcoholic beverage
Abstainer
195
13258.5
1 [Reference]
1 [Reference]
1 [Reference]
Ever user
486
24702
1.47 (1.13 – 1.90)
0.003
1.49 (1.09 – 2.03)
0.01
1.55 (1.11 – 2.15)
0.01
>0 to ≤14 grams
294
15340.5
1.39 (1.04 – 1.86)
0.03
1.50 (1.09 – 2.07)
0.01
1.56 (1.11 – 2.18)
0.01
≥14 grams to ≤28
87
4662.5
1.29 (0.88 – 1.90)
0.19
1.23 (0.78 – 1.94)
0.37
1.25 (0.77 – 2.04)
0.37
≥28 grams
105
4699
1.95 (1.36 – 2.79)
<0.001
1.69 (1.07 – 2.65)
0.02
1.79 (1.12 – 2.87)
0.02
Average daily ethanol intake from any alcoholic beverage (per 14 grams/day)
681
37960.5
1.07 (1.01 – 1.14)
0.03
1.02 (0.94 – 1.09)
0.66
1.02 (0.95 – 1.10)
0.52
Average daily ethanol intake from beerc
Abstainer
330
21864
1 [Reference]
1 [Reference]
1 [Reference]
Ever user
385
18012.5
1.55 (1.24 – 1.94)
<0.001
1.27 (0.94 – 1.73)
0.11
1.23 (0.90 – 1.70)
0.19
Average daily ethanol intake from beer (per 14 grams/day)
715
39876.5
1.10 (1.02 – 1.19)
0.01
1.02 (0.92 – 1.13)
0.72
1.02 (0.92 – 1.12)
0.75
Average daily ethanol intake from wined
Abstainer
475
27673
1 [Reference]
1 [Reference]
1 [Reference]
Ever user
253
12565.5
0.97 (0.76 – 1.24)
0.80
1.01 (0.76 – 1.34)
0.96
1.06 (0.78 – 1.43)
0.71
Average daily ethanol intake from wine (per 14 grams/day)
728
40238.5
0.78 (0.54 – 1.11)
0.18
0.79 (0.54 – 1.15)
0.22
0.87 (0.61 – 1.24)
0.45
Average daily ethanol intake from spiritse
Abstainer
435
26204
1 [Reference]
1 [Reference]
1 [Reference]
Ever user
277
13331.5
1.30 (1.02 – 1.66)
0.04
1.21 (0.91 – 1.61)
0.19
1.27 (0.94 – 1.70)
0.12
Average daily ethanol intake from spirits (per 14 grams/day)
All multivariable models were adjusted for country (categorical, time-fixed), education (categorical, time-fixed l), ascertainment (binary, time-fixed), sex (binary, time-fixed), BMI at age 20 (categorical, time-fixed), diabetes status (binary, time-varying), regular physical activity (binary, time-varying), and smoking status (categorical, time-varying).
All models were additionally adjusted for aspirin or ibuprofen intake (binary, time-fixed), multivitamin intake (binary, time-fixed), calcium intake (binary, time-fixed), and folic acid intake (binary, time-fixed)
Multivariable models additionally adjusted for average daily ethanol intake from wine (binary, time-varying) and from spirits (binary, time-varying)
Multivariable models additionally adjusted for average daily ethanol intake from beer (binary, time-varying) and from spirits (binary, time-varying)
Multivariable models additionally adjusted for average daily ethanol intake from beer (binary, time-varying) and from wine (binary, time-varying)
Hazard ratios for associations between alcohol consumption and the risk of colorectal cancer for DNA mismatch repair gene germline mutation carriers by cancer site
Colona
Rectumb
Multivariable modelc
Multivariable modelc
Cases
Person-years
HR (95% CI)
P value
Cases
Person-years
HR (95% CI)
P value
Average daily ethanol intake from any alcoholic beverage
Abstainer
157
12455.5
1 [Reference]
35
9002.5
1 [Reference]
Ever user
410
23048
1.76 (1.27 – 2.46)
0.001
69
15741
0.79 (0.41 – 1.53)
0.49
>0 to ≤14 grams
252
14459.5
1.78 (1.27 – 2.49)
0.001
38
10123
0.79 (0.39 – 1.59)
0.50
>14 grams to ≤28 grams
75
4338.5
1.51 (0.91 – 2.49)
0.11
10
2862
0.51 (0.19 – 1.40)
0.19
>28 grams
83
4250
1.94 (1.19 – 3.18)
0.01
21
2756
1.14 (0.45 – 2.89)
0.79
Average daily ethanol intake from any alcoholic beverage (per 14 grams/day)
567
35503.5
1.01 (0.94 – 1.09)
0.76
104
24743.5
1.08 (0.88 – 1.31)
0.47
Average daily ethanol intake from beerd
Abstainer
275
20705
1 [Reference]
51
15245
1 [Reference]
Ever user
321
16579.5
1.36 (0.97 – 1.91)
0.08
58
10759.5
1.03 (0.60 – 1.78)
0.91
Average daily ethanol intake from beer (per 14 grams/day)
596
37284.5
0.99 (0.89 – 1.10)
0.90
109
26004.5
1.16 (0.91 – 1.48)
0.24
Average daily ethanol intake from winee
Abstainer
386
25671
1 [Reference]
82
17604.5
1 [Reference]
Ever user
221
11959.5
1.09 (0.80 – 1.48)
0.59
29
8609
0.63 (0.29 – 1.36)
0.24
Average daily ethanol intake from wine (per 14 grams/day)
607
37630.5
0.79 (0.53 – 1.18)
0.26
111
26213.5
0.74 (0.29 – 1.87)
0.52
Average daily ethanol intake from spiritsf
Abstainer
362
24531
1 [Reference]
67
17020.5
1 [Reference]
Ever user
234
12510.5
1.27 (0.93 – 1.73)
0.13
39
8615
0.94 (0.48 – 1.84)
0.86
Average daily ethanol intake from spirits (per 14 grams/day)
Number of colon cancers: 643; participants with synchronous colon and rectal cancers (n=10) were excluded from analysis
Number of rectal cancers: 116; participants with synchronous colon and rectal cancers (n=10) were excluded from analysis
All multivariable models were adjusted for country (categorical, time-fixed), education (categorical, time-fixed), ascertainment (binary, time-fixed), sex (binary, time-fixed), BMI at age 20 (categorical, time-fixed), diabetes status (binary, time-varying), regular physical activity (binary, time-varying), and smoking status (categorical, time-varying)
Models additionally adjusted for average daily ethanol intake from wine (binary, time-varying) and from spirits (binary, time-varying)
Models additionally adjusted for average daily ethanol intake from beer (binary, time-varying) and from spirits (binary, time-varying)
Models additionally adjusted for average daily ethanol intake from beer (binary, time-varying) and from wine (binary, time-varying)