Diet-wide analyses for risk of colorectal cancer: prospective study of 12,250 incident cases among 543,000 women in the UK

Diet-wide analyses for risk of colorectal cancer: prospective study of 12,250 incident cases among 543,000 women in the UK | medRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-P4HH5NV'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search Diet-wide analyses for risk of colorectal cancer: prospective study of 12,250 incident cases among 543,000 women in the UK View ORCID Profile Keren Papier , Kathryn E Bradbury , Angela Balkwill , Isobel Barnes , View ORCID Profile Karl Smith-Byrne , Marc J. Gunter , Sonja I Berndt , Loic Le Marchand , Anna H Wu , Ulrike Peters , Valerie Beral , Timothy J Key , Gillian K Reeves , the Million Women Study Collaborators doi: https://doi.org/10.1101/2024.06.12.24308822 Keren Papier 1 Cancer Epidemiology Unit, Nuffield Department of Population Health, University of Oxford , UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Keren Papier For correspondence: Keren.Papier{at}ndph.ox.ac.uk Kathryn E Bradbury 2 Department of Epidemiology and Biostatistics, School of Population Health, Faculty of Medical and Health Sciences, The University of Auckland , New Zealand Find this author on Google Scholar Find this author on PubMed Search for this author on this site Angela Balkwill 1 Cancer Epidemiology Unit, Nuffield Department of Population Health, University of Oxford , UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site Isobel Barnes 1 Cancer Epidemiology Unit, Nuffield Department of Population Health, University of Oxford , UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site Karl Smith-Byrne 1 Cancer Epidemiology Unit, Nuffield Department of Population Health, University of Oxford , UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Karl Smith-Byrne Marc J. Gunter 3 Cancer Epidemiology and Prevention Research Unit, Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London , UK 4 Nutrition and Metabolism Branch, International Agency for Research on Cancer , Lyon, France Find this author on Google Scholar Find this author on PubMed Search for this author on this site Sonja I Berndt 5 Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health , Bethesda, Maryland, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Loic Le Marchand 6 University of Hawaii Cancer Center , Honolulu, Hawaii, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Anna H Wu 7 University of Southern California, Department of Population and Public Health Sciences , Los Angeles, California, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Ulrike Peters 8 Division of Public Health Sciences, Fred Hutchinson Cancer Center , Seattle, WA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Valerie Beral 1 Cancer Epidemiology Unit, Nuffield Department of Population Health, University of Oxford , UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site Timothy J Key 1 Cancer Epidemiology Unit, Nuffield Department of Population Health, University of Oxford , UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site Gillian K Reeves 1 Cancer Epidemiology Unit, Nuffield Department of Population Health, University of Oxford , UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site Abstract Full Text Info/History Metrics Supplementary material Data/Code Preview PDF Abstract Background Apart from alcohol and processed meat, uncertainty remains regarding the role of diet in the aetiology of colorectal cancer. We conducted a large diet-wide association study of colorectal cancer risk, together with a targeted genetic analysis. Methods We examined associations of 97 foods and nutrients with risk of colorectal cancer in 542,778 women enrolled in the Million Women Study using multivariable-adjusted Cox regression models. We also assessed the association between genetically predicted milk intake (as a proxy of dairy and/or calcium intake) and colorectal cancer risk in the ColoRectal Transdisciplinary Study, the Colon Cancer Family Registry, and the Genetics and Epidemiology of Colorectal Cancer consortium (GECCO) using a two-sample Mendelian randomisation (MR) analysis. Results During a mean (SD) 16.6 (4.8) years of follow-up, 12,251 women were diagnosed with colorectal cancer. Seventeen dietary factors were associated with risk of colorectal cancer (False Discovery Rate <0.05). Of these associations, alcohol and calcium intakes had the strongest associations with colorectal cancer risk; a positive association for alcohol (relative risk [RR] per 20 g/day=1.15, 95% confidence interval [CI] 1.09-1.20, p<0.0000001) and an inverse association for calcium (RR per 300 mg/day=0.83, 95% CI 0.77-0.89, p<0.000001). Other dairy-related factors including dairy milk, yogurt, riboflavin, magnesium, phosphorus, and potassium were also inversely associated with colorectal cancer risk, though further analysis showed that calcium intake was likely to account for these associations. Of the remaining dietary factors that were associated with colorectal cancer risk, only red and processed meat intake was associated with increased risk. Breakfast cereal, fruit, wholegrains, carbohydrates, fibre, total sugars, folate, and vitamin C were inversely associated with risk, though these associations may have been influenced by residual confounding by lifestyle and other dietary factors. In MR analyses, genetically predicted milk consumption was inversely associated with risk of colorectal cancer (RR per 200 g/day=0.60, 95% CI 0.46-0.74), colon cancer (RR per 200 g/day=0.60, 95% CI 0.43-0.77), and rectal cancer (RR per 200 g/day=0.49, 95% CI 0.31-0.67). Discussion This comprehensive diet-wide study provides robust evidence for the protective role of dairy milk and dairy products in colorectal cancer incidence, which is likely to be driven largely if not wholly by calcium. More research is needed to understand the potential health impacts of increasing calcium intake in some populations. Introduction Colorectal cancer is the third most common cancer in the world, with an estimated 1,926,425 incident cases in 2022 [ 1 ]. The incidence rates vary markedly, with higher rates in high income countries including most European countries, North America, Australia, New Zealand and Japan, and lower rates in low income countries including much of Africa and south Asia [ 1 ], although the rates in lower incidence areas appear to be increasing [ 2 ]. In addition, colorectal cancer rates in migrants have been shown to change within as little as just over a decade towards those of their adopted country [ 3 ], indicating that lifestyle and environmental factors are involved in the aetiology of this cancer. The International Agency for Research on Cancer (IARC) has classified alcoholic beverages and processed meat as carcinogenic to humans (Group 1) and red meat as probably carcinogenic (Group 2A), with the evidence for this classification being based partly (alcohol), or largely (red and processed meat), on the findings for colorectal cancer. The World Cancer Research Fund (WCRF)/American Institute for Cancer Research (AICR)’s third expert report similarly concluded that there is convincing evidence that higher intakes of alcohol and processed meat increase the risk of colorectal cancer; they also concluded that higher consumption of dairy products, dairy milk, calcium, calcium supplements, wholegrains, and foods containing dietary fibre ‘probably’ reduce the risk of colorectal cancer, while higher intake of red meat ‘probably’ increases risk. The evidence for other foods, nutrients and beverages is inconclusive [ 4 - 7 ]. The lack of consensus regarding the relationships between dietary factors other than alcohol and processed meat and colorectal cancer risk may be due, at least in part, to the relatively few studies publishing comprehensive results on all food types [ 4 ], dietary measurement error [ 7 , 8 ], and/or small sample sizes [ 4 ]. In order to address some of these limitations, we report here on a systematic analysis of 97 dietary factors and subsequent colorectal cancer risk using a diet-wide association study [ 5 , 9 ] based on data from a large prospective study of 542,778 UK women who completed a detailed dietary questionnaire, of whom 7% also completed at least one subsequent 24-hour online dietary assessment. We also present complementary findings from a Mendelian randomisation analysis of milk consumption, using data from the ColoRectal Transdisciplinary Study, the Colon Cancer Family Registry, and the Genetics and Epidemiology of Colorectal Cancer consortium (GECCO) Methods Study population Between 1996-2001, 1.3 million women with a mean (SD) age of 56 (6) years who were invited to the National Health Service (NHS) Breast Screening Programme in England and Scotland joined the Million Women Study by completing the recruitment questionnaire, which collected information on demographic, lifestyle and social factors. Participants have been resurveyed at approximately 3-5 year intervals since recruitment, to update information on key exposures and to obtain additional information on new exposures of interest. The study was approved by the Oxford and Anglia Multi-Centre Research Ethics Committee and all participants gave written consent for follow-up through medical records. Further details of the study protocol and questionnaires have been published and the questionnaires can be viewed on the Million Women Study website [ 10 , 11 ]. Assessment of diet The current analysis was based on the first resurvey (referred to as baseline) which was conducted around 3 years after recruitment (median year 2001, IQR 3) because this was the first questionnaire when women were asked about their dietary habits. This questionnaire asked participants about their diet during a typical week, including 130 quantitative or semi-quantitative questions on frequency of intake of specific foods and food groups (see Supplementary Methods). The mean daily intakes of nutrients were calculated by multiplying the frequency of consumption of each food or beverage by a specified portion size and the nutrient composition of that particular item. The short term repeatability of most of the diet questions was high, and comparison with estimates from 7-day diet diaries showed moderately good agreement (the median correlation for macronutrient intakes was 0.48, and for alcohol, calcium and fibre the correlations were 0.75, 0.62, and 0.62, respectively) [ 12 ]. Repeat measures of dietary intake were also collected through a web-based 24-hour dietary questionnaire (the Oxford WebQ) in a sub-sample (7%) of all women who completed the dietary questionnaire, at an average of ∼10 years after baseline and before the end of follow-up [ 13 ]. In total, we included 97 dietary factors in our diet-wide analysis. Selection of foods and nutrients depended on their availability in both the dietary survey and the Oxford WebQ to ensure repeat measures of intake were available. Supplementary Table 1 presents the mean (SD) intakes for the quantitatively assessed foods and nutrients in women who completed one or more Oxford WebQs. Ascertainment of colorectal cancer Participants were followed by electronic record linkage to routinely collected National Health Service (NHS) data on cancer registrations, deaths and emigrations, coded according to the International Classification of Diseases, 10 th revision (ICD-10). The main endpoint for this study was incident colorectal cancer (ICD-10 C18-C20) and colorectal cancers were further classified as proximal (ICD-10 C18.0-C18.4), distal (C18.5-C18.7), or rectal (C19-C20) cancers. Exclusions and inclusions In total 866,535 women completed the baseline dietary questionnaire and had linked data for cancer and death. Of these, we excluded: 48,151 women with previous registration for malignant cancer (other than non-melanoma skin cancer, C44) or no follow-up before the dietary questionnaire completion date; 5,090 women whose energy intake was outside the range 2093-14,654 KJ per day; 122,689 women who reported having changed their diet due to illness; and 147,827 women with missing data on any semi-quantitative dietary variables (including meat types, fish types, main carbohydrate sources, eggs, vegetables, fruit, sweets, dairy, alcohol, and other beverages) leaving 542,778 women in the final analysis dataset. The characteristics of the women together with details of follow-up are shown in Table 1 . View this table: View inline View popup Download powerpoint Table 1. Characteristics of 542,778 women at baseline, and details of follow-up Statistical analysis For 61 foods and nutrients for which there was a quantitative measure of intake we calculated trends in risk of colorectal cancer per increment in grams, milligrams or micrograms per day using information collected in the Oxford WebQ in women who had not developed colorectal cancer at the time of completing the WebQ. To do this all baseline dietary intakes were first divided into categories using quintiles (or four to five other appropriate cut-points for foods with non-continuous intake distributions). We then derived repeat measures of intake within each baseline category by calculating the mean intakes for each food or nutrient category in women who had completed at least one 24-hour dietary assessment using the Oxford WebQ (based on the first completed Oxford WebQ where more than one had been completed), and assigning these mean intakes for all women in that category. These re-measured mean intakes for each baseline category were then used to calculate trends in risk across categories of baseline intakes (see Supplementary Methods for further description). Trend increments were selected based on the observed differences in WebQ derived intake between the lowest and highest baseline category. This approach reduces the impact of regression dilution bias and other forms of measurement error [ 14 ] and has previously been used for diet research in this cohort [ 15 ] and in the UK Biobank [ 16 ]. The remaining 35 foods (which included fruit and vegetable subtypes, ice cream, legumes, and soy milk) were divided into two categories of intake (‘weekly’ vs ‘less than weekly’); for these foods, risk of colorectal cancer was calculated for high versus low intakes. We examined associations between each of the 97 dietary factors and colorectal cancer incidence separately using Cox proportional hazards regression models. Women-years were calculated from the date when diet was reported up to whichever came first: diagnosis of cancer, emigration, death, or the end of follow up (31 st December 2020). All analyses were stratified by year of birth, date of completion of the 3-year re-survey, and region of residence (ten geographical regions: 9 in England and 1 in Scotland), and adjusted for area-based deprivation (fifths, based on the Townsend deprivation score at recruitment, unknown), highest educational qualification (none, technical, secondary, tertiary, unknown), body mass index ( 35 kg/m 2 , unknown), height ( once per week, unknown), dietary energy intake (except for the analysis of energy and risk; fifths, unknown), alcohol (except for the analyses of alcohol and risk; none, 1-5, 6-10, ≥ 11 drinks per week, unknown), smoking (never, past, current 1–4, current 5–9, current <10, current 10–14, current 15–19, current 20–24, current 25–29, current ≥30 cigarettes per day, unknown), current use of hormonal therapy for menopause (no, yes, unknown), and family history of large bowel cancer (no or unknown, yes). Data were missing for fewer than 5% of women for each of the adjustment variables, with the exception of BMI (6.8% missing data); to ensure that the same women were being compared in all analyses the small number with a missing value for each particular variable were assigned to a separate category for that variable and included in the regression analysis. We used the Benjamini-Hochberg approach to calculate the False Discovery Rate (FDR) at 0.05 to account for multiple testing [ 17 ]. In total, 17 dietary factors met the FDR threshold and these factors were selected for further analyses. Further analyses We assessed the pairwise correlations between the 17 FDR-significant dietary factors. We also examined the associations of these 17 FDR-significant dietary factors with risk of colorectal cancer by categories of intakes at baseline. To investigate the potential role of confounding by lifestyle factors in the associations between these 17 dietary factors and colorectal cancer, we calculated the change in the log relative risk associated with each of the 17 dietary factors after differing levels of adjustment for potential confounders. We also investigated the degree to which each of the 17 dietary associations were independent of the four dietary factors that were most strongly related to risk (calcium, dairy milk, fruit, and wholegrains), by assessing the impact of further adjustment for each of these four factors individually. Dairy milk, and dietary calcium To investigate the separate, independent associations of total dietary calcium and dairy milk with the risk of colorectal cancer we used the residuals method [ 18 ]. To do this, we first obtained the calcium and dairy milk residuals from two separate linear regressions: one regression of dietary calcium on dairy milk, and the other of dairy milk on dietary calcium. The dietary calcium and dairy milk residual values were divided into quintiles. We then compared the associations between dairy milk and colorectal cancer risk using the fully adjusted Cox regression models used in the main analysis with and without adding the quintiles of dietary calcium residuals using likelihood-ratio tests (LRT). The same analysis was repeated for the association between dietary calcium and colorectal cancer by adding and removing the dairy milk residual quintiles. We additionally investigated the associations of dietary calcium with risk of colorectal cancer according to whether it was derived from dairy sources or non-dairy sources and compared the associations using a test for heterogeneity. Sensitivity analyses To explore the possibility of reverse causality, we restricted the analyses for the 17 FDR-significant dietary factors with colorectal cancer risk to women in self-reported excellent or good health at baseline, and to risk of colorectal cancer in the period five or more years after baseline. We also examined potential differences in associations by cancer sub-site including proximal colon, distal colon and rectum. We further assessed associations between the 17 FDR-significant dietary factors and colorectal cancer stratified by smoking status, BMI, area-based deprivation and alcohol intake to investigate residual confounding and potential differences by strata. Mendelian randomisation (MR) using lactase polymorphism Given the strong and consistent association of dairy products, dairy milk, and calcium with a lower risk for colorectal cancer in previous studies [ 4 ] and in the present analysis, we further assessed evidence of causality using MR. Dietary calcium intake does not have an established genetic variant to estimate causal associations, but dairy milk intake in populations of European ancestry is robustly predicted by the SNP rs4988235 [ 19 ] located in the MCM6 gene. This SNP is immediately upstream of the LCT gene that codes for the lactase enzyme necessary to digest the lactose in dairy milk, and the “lactase persistence” genotype is associated with persistence of intestinal lactase production into adulthood [ 20 ]. Dairy milk intake is a large contributor of calcium in European populations, with ∼one third of all calcium coming from dairy milk in the MWS and so genetically predicted milk intake can also be taken as a proxy instrument for calcium intake. We conducted a two-sample MR using a Wald ratio to estimate the associations of SNP rs4988235 with risk for colorectal, colon and rectal cancers. We assigned each additional genetically predicted milk intake increasing allele an increment of 17.1 g/d of dairy milk based on findings from a European cohort study including ∼21,900 participants [ 21 ], and then rescaled this increment to 200 g/d. Summary statistics for the associations of the LCT variant (rs4988235) with colorectal cancer were obtained from a GWAS of 99,152 participants (52,865 colorectal cancer cases and 46,287 controls). The GWAS data were from a meta-analysis within the ColoRectal Transdisciplinary Study, the Colon Cancer Family Registry, and the Genetics and Epidemiology of Colorectal Cancer consortium (GECCO), making this combined analysis the largest meta-analysis for colorectal cancer in adults of European ancestry. Imputation was performed using the Haplotype Reference Consortium r1.0 reference panel and the regression models were further adjusted for age, sex, genotyping platform (when appropriate), and genomic principal components [ 19 ]. All of the statistical analyses in the present study were performed using Stata statistical software 18.1 (StataCorp, College Station, TX) and R 4.1. Results These 542,778 women had a mean (SD) of 16.6 (4.8) years of follow-up, during which 12,251 women were diagnosed with incident colorectal cancer. Figure 1 and Supplementary Table 2 show the RRs for colorectal cancer in relation to intakes of the 97 dietary factors, of which 17 were associated with risk of colorectal cancer (FDR corrected p-value <0.009). Of these 17 dietary factors, alcohol and calcium intakes had the strongest associations (based on lowest p value) with colorectal cancer risk; with a positive association for alcohol (relative risk [RR] per 20 g/day=1.15, 95% confidence interval [CI] 1.09-1.20, p<0.0000001) and an inverse association for calcium (RR per 300 mg/day=0.83, 95% CI 0.77-0.89, p<0.000001). Dairy milk, yogurt, riboflavin, magnesium, phosphorus, and potassium intakes were inversely associated with colorectal cancer risk, as were intakes of breakfast cereal, fruit, wholegrains, carbohydrates, fibre, total sugars, folate, and vitamin C. Red and processed meat intake was positively associated with risk of colorectal cancer (per 30 g/day=1.08, 1.03-1.12, p<0.01). For all of these 17 dietary factors, the categorical RRs and 95% CIs were broadly consistent with their respective log-linear dose response relationships ( Figures 2 and 3 ). Download figure Open in new tab Figure 1. Volcano plot showing results from diet-wide study method evaluating associations between 97 dietary risk factors and colorectal cancer risk The Y axis shows p values for the associations between each of the 97 dietary factors and colorectal cancer incidence separately using Cox proportional hazards regression models stratified by year of birth, date of completion of the 3-year re-survey (which is baseline for this study), and region of residence (10 geographical regions (9 in England), and Scotland), and adjusted for area-based deprivation (fifths, based on the Townsend deprivation score, unknown), highest educational qualification (none, technical, secondary, tertiary, unknown), body mass index (<20, 20-22.49, 22.5-24.9, 25.0-27.49, 27.5-29.9, 30-32.49, 32.5-34.9, 35+ kg/m 2 , unknown), height ( once per week, unknown), dietary energy intake (except for the analysis of energy and risk; fifths, unknown), alcohol (none, 1-5, 6-10, ≥ 11 drinks per week, unknown), smoking (never, past, current 1–4, current 5–9, current <10, current 10‒14, current 15‒19, current 20‒24, current 25‒29, current ≥30 cigarettes per day, unknown), current use of hormonal therapy for menopause (no, yes, unknown), and family history of bowel cancer (no, yes). The X axis shows relative risks and p values (see Supplementary Table 2 for increments). Download figure Open in new tab Figure 2 Associations of the top FDR-significant dietary factors (p<0.001) and colorectal cancer by intake categories Mean daily intakes taken from the mean of the 24-hour dietary assessments. Wholegrain intake represents actual grams of wholegrains. Associations between each of the foods or nutrients and colorectal cancer incidence separately using Cox proportional hazards regression models stratified by year of birth, date of completion of the 3-year re-survey (which is baseline for this study), and region of residence (10 geographical regions (9 in England), and Scotland), and adjusted for area-based deprivation (fifths, based on the Townsend deprivation score, unknown), highest educational qualification (none, technical, secondary, tertiary, unknown), body mass index (<20, 20-22.49, 22.5-24.9, 25.0-27.49, 27.5-29.9, 30-32.49, 32.5-34.9, 35+ kg/m2, unknown), height ( once per week, unknown), dietary energy intake (except for the analysis of energy and risk; fifths, unknown), alcohol (none, 1-5, 6-10, ≥ 11 drinks per week, unknown), smoking (never, past, current 1–4, current 5–9, current <10, current 10–14, current 15–19, current 20–24, current 25–29, current ≥30 cigarettes per day, unknown), current use of hormonal therapy for menopause (no, yes, unknown), and family history of bowel cancer (no, yes). Download figure Open in new tab Figure 3 Associations of the less FDR-significant dietary factors and colorectal cancer (p<0.01) by intake categories Mean daily intakes taken from the mean of the 24-hour dietary assessments. Associations between each of the foods or nutrients and colorectal cancer incidence separately using Cox proportional hazards regression models stratified by year of birth, date of completion of the 3-year re-survey (which is baseline for this study), and region of residence (10 geographical regions (9 in England), and Scotland), and adjusted for area-based deprivation (fifths, based on the Townsend deprivation score, unknown), highest educational qualification (none, technical, secondary, tertiary, unknown), body mass index (<20, 20-22.49, 22.5-24.9, 25.0-27.49, 27.5-29.9, 30-32.49, 32.5-34.9, 35+ kg/m2, unknown), height ( once per week, unknown), dietary energy intake (except for the analysis of energy and risk; fifths, unknown), alcohol (none, 1-5, 6-10, ≥ 11 drinks per week, unknown), smoking (never, past, current 1–4, current 5–9, current <10, current 10–14, current 15–19, current 20–24, current 25–29, current ≥30 cigarettes per day, unknown), current use of hormonal therapy for menopause (no, yes, unknown), and family history of bowel cancer (no, yes). The pairwise correlations for the 17 FDR-significant dietary factors are displayed in Table 2 . Dairy-related foods and nutrients had the strongest pairwise correlations (calcium, phosphorus, riboflavin, dairy milk, magnesium, potassium). We also observed strong and moderately-strong (r >0.5) pairwise correlations between fibre-related foods and nutrients (including carbohydrates, total sugars, magnesium, fibre, folate, wholegrains, vitamin C, and fruit). Dairy-related and fibre-related foods and nutrients also had moderate (r>0.35) pairwise correlations between them. In contrast, alcohol, and red and processed meat were generally only very weakly correlated with the other dietary factors. View this table: View inline View popup Download powerpoint Table 2. Pairwise correlations among FDR-significant dietary factors (by order of p value for association) Progressive adjustment for potential lifestyle confounders across the models did not substantially change the log relative risks for the FDR-significant dietary factors with the strongest associations (based on lowest p values) with risk of colorectal cancer (alcohol, calcium, dairy milk) though did lead to a slight increase in the magnitude of the associations. Conversely, progressive adjustment led to attenuation of the magnitude of some of the associations with other FDR-significant dietary factors (including fruit, wholegrains, breakfast cereal), suggesting that the associations with risk of colorectal cancer for these latter foods may have been at least partly due to residual confounding with lifestyle factors (Supplementary Table 3). Table 3 presents associations of the 17 FDR-significant dietary factors with risk of colorectal cancer, further adjusted for calcium, dairy milk, fruit, and wholegrains. After adjustment for calcium, the inverse associations for dairy milk, phosphorus, riboflavin, magnesium, potassium, yogurt, folate, breakfast cereal and total sugars were no longer evident. Adjustment for dairy milk attenuated the associations for riboflavin, breakfast cereal, and potassium to a lesser extent than did adjustment for calcium and did not completely explain the association of calcium intake with risk, which remained significant. Adjustment for fruit intake also led to attenuation of the associations for phosphorus, riboflavin, magnesium, potassium, folate, carbohydrates, total sugars, vitamin C, breakfast cereal, and fibre, and adjustment for wholegrains attenuated the associations for magnesium, breakfast cereal and fibre, to the extent that none of these associations remained significant after adjustment. Adjusting for calcium, dairy milk, fruit, or wholegrains minimally affected the associations for wholegrains, alcohol and red and processed meat ( Table 3 ). View this table: View inline View popup Download powerpoint Table 3. Associations of FDR-significant dietary factors with risk of colorectal cancer, further adjusted for calcium, dairy milk, fruit, and wholegrains View this table: View inline View popup Download powerpoint Table 4. Associations of sources of dietary calcium with colorectal cancer risk Given the high correlation between calcium and dairy milk, we investigated the association of each, independently of the other, using the residuals method. Calcium intake was independently associated with risk of colorectal cancer whereas dairy milk intake was not; LRT=6.39 (p=0.01) for the difference between the models before and after adding the estimated residuals for calcium intake, and LRT=0.18 (p=0.67) for the difference between the models before and after adding the estimated residuals for dairy milk intake. Supplementary analyses In supplementary analyses restricted to women who self-reported excellent or good health at baseline, and to risk of colorectal cancer in the period 5 or more years after baseline, the findings were broadly similar (Supplementary Table 4). We also found no significant heterogeneity in the observed associations for the FDR-significant dietary factors by cancer sub-site, except for alcohol, which appeared to be less detrimental in the proximal colon and most harmful in the rectum (Supplementary Table 5; phet by subsite=0.02). In analyses stratified by smoking status, BMI category, area-based deprivation and alcohol intake, we observed stronger associations for dairy milk (p=0.005) and riboflavin (p=0.006) in never smokers and a stronger association for wholegrains (p=0.04) in those with a lower BMI (Supplementary Table 6). MR using lactase polymorphism SNP We observed an inverse association of genetically predicted milk consumption and risk of colorectal cancer that was larger than the inverse association with reported dairy milk intake and colorectal cancer: RR per 200 g/day=0.60, 0.46-0.74; colon cancer: RR per 200 g/day=0.60, 0.43-0.77; and rectal cancer: RR per 200 g/day=0.49, 0.31-0.67. Discussion In this single largest prospective study of diet and colorectal cancer, we found a marked positive association for alcohol, and a strong inverse association for calcium, further supported by an accompanying analysis for genetically-predicted milk intake (a proxy of calcium intake) and inverse associations with other dairy-related factors including dairy milk, yogurt, riboflavin, magnesium, phosphorus, and potassium which appeared to be primarily due to their association with calcium in further analyses. We also found a positive association for red and processed meat intake that was minimally affected by confounding by diet and lifestyle factors. In addition we observed inverse associations with risk for breakfast cereal, fruit, wholegrains, carbohydrates, fibre, total sugars, folate, and vitamin C, but these inverse associations may have been influenced by residual confounding by lifestyle and other dietary factors. Our study recapitulates the well-established positive association between alcohol consumption and risk of colorectal cancer [ 10 ] and is in line with the 2018 WCRF dose-response meta-analysis which found a seven percent higher risk of colorectal cancer per 10 grams of alcohol per day (equivalent to 14% per 20 grams of alcohol per day) [ 4 ] which is of nearly identical magnitude to the 15% higher risk we observed per 20 grams per day. Previous MR studies in adults of Asian and European ancestry also support a causal association of alcohol intake with colorectal cancer risk [ 22 - 24 ]. Suggested mechanisms by which alcohol could increase the risk of colorectal cancer include the production of acetaldehyde, found to be mutagenic in high concentrations, which has been shown to disrupt deoxyribonucleic (DNA) repair function in human tissue and experimental animal studies [ 25 ], and increase generation of carcinogenic reactive oxygen species [ 26 ]. Our findings with respect to dairy-related foods and nutrients are consistent with those from the most recent WCRF review which judged that dairy products (including evidence for total dairy, milk, and cheese, as well as dietary calcium) and calcium supplements probably decrease the risk of colorectal cancer [ 4 ]. Of the dairy-related foods and nutrients examined in the present study, all were inversely associated with risk of colorectal cancer, except for cheese and ice-cream. Our findings specifically for calcium (17% lower risk per 300 mg/day) and dairy milk (14% lower risk per 200 g/day), are larger in magnitude than those reported in the 2018 WCRF dose-response meta-analysis (9% and 6%, respectively for the same increments). In subsequent studies, a diet-wide analysis in the EPIC study (∼5000 cases among 387,000 participants) found 7% and 5% lower risks for the same increments [ 5 ], a study in the Nurses’ Health Study II (349 cases among 94,000 participants) found a 15% lower risk for calcium per 300 mg/day [ 27 ], and a UK Biobank study (∼2600 cases among 476,000 participants) [ 16 ] reported a 14% lower risk per 200 ml dairy milk/day (although this was not formally statistically significant, p for trend 0.07). One study in the China Kadoorie Biobank (3,350 cases among 510,146 participants), with a much lower dairy intake than in western cohorts, found a suggestive positive association between dairy intake (largely coming from dairy milk) and colorectal cancer risk (eight percent higher risk per 50 g/day) [ 28 ]; it is possible that the association between dairy milk and colorectal cancer risk might differ in populations where a large majority cannot digest lactose, such as that in the China Kadoorie Biobank [ 29 , 30 ]. Our MR findings for genetically predicted milk intake in a European population provide evidence for a causal association of dairy and/or dietary calcium, adding to that from previous MR studies with similar findings based on far fewer colorectal cancer cases (i.e. ∼7000 cases [ 31 ] and ∼3400 [ 32 ]) than our analysis (∼53,000 cases). The MR findings for genetically predicted dairy milk were of much larger magnitude than what we observed in the observational analyses (40% versus 14% lower risk per 200 g/day), though genetically predicted intake represents the effect of lactase exposure throughout adult life, so this might be expected [ 33 ]. The associations we observed for dairy milk and the other dairy-related foods and nutrients with colorectal cancer are likely largely or wholly driven by calcium intake; this is based on the low p-value for the association between calcium and colorectal cancer risk, the large impact we found when adjusting the dairy-related food and nutrient associations with colorectal cancer for calcium, our residual-based analyses which showed that dairy milk was not independently associated with colorectal cancer risk after taking calcium residuals into account, and the investigation of the association of calcium and colorectal cancer risk by dietary source, which provided no evidence for heterogeneity of association with colorectal cancer risk by calcium source. The probable protective role of calcium may relate to its ability to bind to bile acids and free fatty acids in the colonic lumen, thereby lowering their potentially carcinogenic effects [ 34 , 35 ]. Furthermore, experimental work in rats has shown that having higher levels of dietary calcium in the colonic lumen reduces colonic permeability, particularly if dietary phosphate levels are also high, thereby helping protect the intestinal mucosa from being injured by potentially harmful luminal contents (e.g. bile acids) [ 36 ]. Other experimental work suggests that calcium may also have direct effects on colonic tissue, for example, calcium may promote colorectal epithelial cell differentiation [ 37 ], enhance apoptosis, and reduce DNA oxidative damage in the colorectal mucosa [ 38 ]. Laboratory studies also suggest that dietary calcium may reduce the incidence of KRAS mutations in the colon [ 39 ]. The results from these previous experimental studies suggest that the potential protective effects of calcium appear to be related to its presence in the intestinal lumen. There is limited evidence on the role of circulating calcium in colorectal cancer risk, with the available genetic and observational evidence suggesting no clear association [ 27 , 40 , 41 ], though circulating concentrations of calcium are tightly regulated in the body and unlikely to be materially affected by moderate variations in dietary intake [ 42 ]. If the protective role of dairy milk and the other dairy-related foods is not wholly attributed to its calcium content, other possible mechanisms may relate to conjugated linoleic acid, butyric acid, and sphingomyelin which are present in dairy milk and have been shown to inhibit chemically-induced colon carcinogenesis in some animal models [ 43 - 47 ]. We could not investigate the association for calcium supplements in the present study. A recent meta-analysis of six cohort studies found that a 300 mg per day increase in calcium from supplements was associated with a 9% lower risk of incident colorectal cancer [ 48 ] but a randomised controlled trial in 36,282 postmenopausal women of supplementation with 1000 mg of elemental calcium (as calcium carbonate) with 40 µg of vitamin D3 daily for 7 years found no significant impact on risk [ 49 ]. However, mean calcium intakes in these women were relatively high at enrolment; the average intake from diet plus supplements was ∼1100 mg/day, similar to the mean intake in the highest quintile of intake in the present study. It is therefore possible that the baseline calcium intakes in this trial were already high enough that the intervention with supplemental calcium had no further impact on colorectal cancer risk. Additionally, colorectal cancer has a long latency period, so it is possible that a follow-up period of seven years may have been insufficient to detect an effect of the intervention [ 50 ]. Apart from alcohol, the only dietary factor which was positively associated with colorectal cancer risk in these data was red and processed meat consumption. We found an 11% higher risk of colorectal cancer per 30 g/day higher red and processed meat consumption; this is equivalent to a 42% higher risk per 100 g/day, which is substantially larger than the 12% higher risk per 100 g/day reported in the 2018 WCRF dose-response meta-analysis [ 4 ]. This larger association might be partly explained by our use of repeat dietary intake measures to reduce the impact of measurement error and regression dilution bias. Similar to the WCRF report, we found a larger association for processed meat than for red meat, although the independent associations for red meat and processed meat separately were not robust to correction for multiple testing. However, in this paper we explored 97 dietary variables and corrected for multiple testing, and there are strong pre-existing hypotheses and evidence for some dietary factors, including for red and processed meat, and therefore correcting for multiple testing may have been a stringent approach for such dietary factors with consistent evidence of an association. Several mechanisms have been proposed to explain the positive associations observed for red and processed meat including haem iron, which may catalyse the formation of N-nitroso compounds that have been found to generate mutations in the colon [ 51 ], cooking meat at high temperatures which forms heterocyclic amines and polycyclic aromatic hydrocarbons [ 52 ], and meat smoking or adding sodium nitrites or nitrates for preservation which can lead to the exogenous formation of N-nitroso compounds [ 52 - 54 ]. The magnitudes of the lower risks of colorectal cancer associated with greater intakes of breakfast cereal, fruit, wholegrains, carbohydrates, fibre, total sugars, folate, and vitamin C observed in this cohort were relatively small, and these inverse associations were affected by confounding by lifestyle factors and dietary factors. Suggested mechanisms for these inverse associations relate to wholegrains [ 55 - 57 ] and dietary fibre [ 4 ]. Wholegrains are a rich source of fibre and previous trial evidence shows that dietary fibre increases stool bulk; this leads to reduced transit time and dilutes the contents of the large bowel, thus possibly also diluting carcinogenic substances in bowel contents and the time such carcinogens are present in the colon [ 58 ]. Additionally, dietary fibre is fermented in the colon, forming short chain fatty acids such as butyrate, which reduce intestinal pH [ 59 ] and thus inhibit the conversion of primary bile acids into secondary bile acids, which promote cell proliferation [ 35 ]. It is also possible that other compounds found in these foods may have protective effects [ 4 , 60 ] [ 61 ]. In this diet-wide prospective study on diet and colorectal cancer, we comprehensively investigated nearly 100 dietary factors in the same cohort, thereby reducing exposure selection bias, ensuring standardisation of confounding adjustment, and increasing the specificity of our findings [ 62 ]. We took a rigorous approach to explore the possibility of reverse causation by excluding women who reported changing their diet in the past 5 years due to illness from all the analyses, and in separate analyses by further restricting to women who self-reported good or excellent health at baseline, and by excluding the first 5 years of follow-up. We also assessed the potential role of residual confounding by assessing the impact of incremental adjustment for key confounders, and by conducting sensitivity analyses restricted to never smokers. The reproducibility and performance of the dietary assessment method used at baseline was assessed by comparison with records from 7-day food diaries [ 12 ]. In addition, we used a web-based 24-hour dietary questionnaire (the Oxford WebQ), validated against recovery biomarkers [ 63 ], to re-measure diet about 10 years later to estimate usual long-term diet and test for trends across baseline categories of intake, thus reducing the impact of measurement error and changes in diet over time [ 14 ]. In addition, the large sample size enabled us to look at proximal colon, distal colon and rectum separately. A limitation of our study was that for some dietary factors the range of re-measured (i.e. long-term) intakes across the extreme baseline groups was small, therefore for these factors we were limited in our ability to detect associations with disease. Also, we were unable to include some dietary items (e.g. butter) due to the format of the dietary survey. Additionally, although the women in the cohort are representative of middle-aged and older women living in the UK, they are predominantly of European ancestry. Therefore, the results are not necessarily generalisable to non-White women or to men, or to populations where a large majority cannot digest lactose (including e.g. many Asian populations). Conclusions In addition to confirming the well-established positive associations of alcohol, and red and processed meat consumption, with risk of colorectal cancer, this large prospective analysis provides robust evidence supporting the protective role of dietary calcium. Additional research is needed to investigate overall health benefits or risks associated with higher calcium intakes. Data Availability Information on data access is available at www.millionwomenstudy.org/data_access/ . https://www.ceu.ox.ac.uk/research/the-million-women-study Funding This work was funded by Cancer Research UK (C570/A16491 and A29186) and the UK Medical Research Council (MR/K02700X/1). KEB’s work on this project was funded by a Girdlers’ Health Research Council Fellowship (3716491). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. For the purpose of open access, the authors have applied a Creative Commons Attribution (CC BY) licence to any Author Accepted Manuscript version arising. Conflict of Interest UP was a consultant with AbbVie and her husband is holding individual stocks for the following companies: BioNTech SE – ADR, Amazon, CureVac BV, NanoString Technologies, Google/Alphabet Inc Class C, NVIDIA Corp, Microsoft Corp. Acknowledgements The authors would particularly like to acknowledge the significant contribution of Professor Dame Valerie Beral for the initiation of the Million Women Study and for her expertise and guidance in this research. She was Chief Investigator of the Study until 2020 and commented on early versions of this manuscript before she died in August 2022. We thank the women who have participated in the Million Women Study as well as the staff from the participating NHS breast screening centres. This work uses data provided by patients and collected by the NHS as part of their care and support. We thank NHS England and Public Health Scotland for the health outcomes data. Footnotes ↵ * Shared first author ↵ ± Deceased References 1. ↵ Globocan, Cancer Today . Cancer Fact sheets: Colorectal cancer. Absolute numbers, Incidence , Both sexes, in 2022 . 2022 . 2. ↵ Globocan, Cancer Over time . Cancer Fact sheets: Colorectal cancer . Age-standardized rate (World) per 100 000, incidence, males and females . 2022 . 3. ↵ McMichael , A.J. , M.G. McCall , J.M. Hartshorne , and T.L. Woodings , Patterns of gastro-intestinal cancer in European migrants to Australia: the role of dietary change . Int J Cancer , 1980 . 25 ( 4 ): p. 431 – 7 . OpenUrl CrossRef PubMed Web of Science 4. ↵ World Cancer Research Fund and American Institute for Cancer Research, Diet, Nutrition, Physical Activity, and the Prevention of Colorectal Cancer. , in Continuous Update Project Report . 2018 . 5. ↵ Papadimitriou , N. , E. Bouras , P.A. van den Brandt , D.C. Muller , A. Papadopoulou , A.K. Heath , E. Critselis , et al. , A Prospective Diet-Wide Association Study for Risk of Colorectal Cancer in EPIC . Clin Gastroenterol Hepatol , 2021 . 6. Veettil , S.K. , T.Y. Wong , Y.S. Loo , M.C. Playdon , N.M. Lai , E.L. Giovannucci , and N. Chaiyakunapruk , Role of Diet in Colorectal Cancer Incidence: Umbrella Review of Meta-analyses of Prospective Observational Studies . JAMA Netw Open , 2021 . 4 ( 2 ): p. e2037341 . OpenUrl 7. ↵ Papadimitriou , N. , G. Markozannes , A. Kanellopoulou , E. Critselis , S. Alhardan , V. Karafousia , J.C. Kasimis , et al. , An umbrella review of the evidence associating diet and cancer risk at 11 anatomical sites . Nat Commun , 2021 . 12 ( 1 ): p. 4579 . OpenUrl CrossRef 8. ↵ Kirkpatrick , S.I. , R.P. Troiano , B. Barrett , C. Cunningham , A.F. Subar , Y. Park , H.R. Bowles , et al. , Measurement error affecting web- and paper-based dietary assessment instruments: Insights from the Multi-Cohort Eating and Activity Study for Understanding Reporting Error. Am J Epidemiol , 2022 . 9. ↵ Forman , J.P. and W.C. Willett , Nutrient-wide association studies: another road to the same destination . Circulation , 2012 . 126 ( 21 ): p. 2447 – 8 . OpenUrl FREE Full Text 10. ↵ The Million Women Study . 19 January 2022 ]; Available from: http://www.millionwomenstudy.org . 11. ↵ The Million Women Study Collaborative Group, The Million Women Study: design and characteristics of the study population . Breast Cancer Res , 1999 . 1 ( 1 ): p. 73-80 . OpenUrl 12. ↵ Roddam , A.W. , E. Spencer , E. Banks , V. Beral , G. Reeves , P. Appleby , I. Barnes , D.C. Whiteman , and T.J. Key , Reproducibility of a short semi-quantitative food group questionnaire and its performance in estimating nutrient intake compared with a 7-day diet diary in the Million Women Study . Public Health Nutr , 2005 . 8 ( 2 ): p. 201 – 13 . OpenUrl PubMed Web of Science 13. ↵ Liu , B. , H. Young , F.L. Crowe , V.S. Benson , E.A. Spencer , T.J. Key , P.N. Appleby , and V. Beral , Development and evaluation of the Oxford WebQ, a low-cost, web-based method for assessment of previous 24 h dietary intakes in large-scale prospective studies . Public Health Nutr , 2011 . 14 ( 11 ): p. 1998 – 2005 . OpenUrl CrossRef PubMed Web of Science 14. ↵ MacMahon , S. , R. Peto , J. Cutler , R. Collins , P. Sorlie , J. Neaton , R. Abbott , et al. , Blood pressure, stroke, and coronary heart disease . Part 1, Prolonged differences in blood pressure: prospective observational studies corrected for the regression dilution bias. Lancet , 1990 . 335 ( 8692 ): p. 765-74 . OpenUrl 15. ↵ Key , T.J. , A. Balkwill , K.E. Bradbury , G.K. Reeves , A.S. Kuan , R.F. Simpson , J. Green , and V. Beral , Foods, macronutrients and breast cancer risk in postmenopausal women: a large UK cohort . Int J Epidemiol , 2019 . 48 ( 2 ): p. 489 – 500 . OpenUrl CrossRef PubMed 16. ↵ Bradbury , K.E. , N. Murphy , and T.J. Key , Diet and colorectal cancer in UK Biobank: a prospective study . Int J Epidemiol , 2020 . 49 ( 1 ): p. 246 – 258 . OpenUrl PubMed 17. ↵ Benjamini , Y. and Y. Hochberg , Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing . Journal of the Royal Statistical Society: Series B (Methodological ), 1995 . 57 ( 1 ). 18. ↵ Sweetland , S. , S. Floud , K. Gaitskell , and G.K. Reeves , Adiposity and risk of oesophageal cancer subtypes in the Million Women Study . Int J Epidemiol , 2023 . 52 ( 6 ): p. 1795 – 1804 . OpenUrl 19. ↵ Guimarães Alves , A.C. , N.M. Sukow , G. Adelman Cipolla , M. Mendes , T.P. Leal , M.L. Petzl-Erler , R. Lehtonen Rodrigues Souza , et al. , Tracing the Distribution of European Lactase Persistence Genotypes Along the Americas . Front Genet , 2021 . 12 : p. 671079 . OpenUrl CrossRef PubMed 20. ↵ Sun , B.B. , J.C. Maranville , J.E. Peters , D. Stacey , J.R. Staley , J. Blackshaw , S. Burgess , et al. , Genomic atlas of the human plasma proteome . Nature , 2018 . 558 ( 7708 ): p. 73-79 . OpenUrl 21. ↵ Vissers , L.E.T. , I. Sluijs , Y.T. van der Schouw , N.G. Forouhi , F. Imamura , S. Burgess , A. Barricarte , et al. , Dairy Product Intake and Risk of Type 2 Diabetes in EPIC-InterAct: A Mendelian Randomization Study . Diabetes Care , 2019 . 42 ( 4 ): p. 568 – 575 . OpenUrl Abstract / FREE Full Text 22. ↵ Zhou , X. , L. Wang , J. Xiao , J. Sun , L. Yu , H. Zhang , X. Meng , et al. , Alcohol consumption, DNA methylation and colorectal cancer risk: Results from pooled cohort studies and Mendelian randomization analysis . Int J Cancer , 2022 . 151 ( 1 ): p. 83 – 94 . OpenUrl 23. Li , Y. , D. Ye , W. Zhou , B. Liu , Y. Mao , and X. Sun , Alcohol consumption and colorectal cancer risk: A mendelian randomization study . Front Genet , 2022 . 13 : p. 967229 . OpenUrl 24. ↵ Cornish , A.J. , P.J. Law , M. Timofeeva , K. Palin , S.M. Farrington , C. Palles , M.A. Jenkins , et al. , Modifiable pathways for colorectal cancer: a mendelian randomisation analysis . Lancet Gastroenterol Hepatol , 2020 . 5 ( 1 ): p. 55 – 62 . OpenUrl 25. ↵ Seitz , H.K. and F. Stickel , Molecular mechanisms of alcohol-mediated carcinogenesis . Nat Rev Cancer , 2007 . 7 ( 8 ): p. 599 – 612 . OpenUrl CrossRef PubMed Web of Science 26. ↵ Gapstur , S.M. , V. Bouvard , S.T. Nethan , J.L. Freudenheim , C.C. Abnet , D.R. English , J. Rehm , et al. , The IARC Perspective on Alcohol Reduction or Cessation and Cancer Risk . N Engl J Med , 2023 . 389 ( 26 ): p. 2486 – 2494 . OpenUrl 27. ↵ Kim , H. , J. Hur , K. Wu , M. Song , M. Wang , S.A. Smith-Warner , X. Zhang , and E.L. Giovannucci , Total calcium, dairy foods and risk of colorectal cancer: a prospective cohort study of younger US women . Int J Epidemiol , 2023 . 52 ( 1 ): p. 87 – 95 . OpenUrl 28. ↵ Kakkoura , M.G. , H. Du , Y. Guo , C. Yu , L. Yang , P. Pei , Y. Chen , et al. , Dairy consumption and risks of total and site-specific cancers in Chinese adults: an 11-year prospective study of 0.5 million people . BMC Med , 2022 . 20 ( 1 ): p. 134 . OpenUrl 29. ↵ He , T. , K. Venema , M.G. Priebe , G.W. Welling , R.J. Brummer , and R.J. Vonk , The role of colonic metabolism in lactose intolerance . Eur J Clin Invest , 2008 . 38 ( 8 ): p. 541 – 7 . OpenUrl CrossRef PubMed 30. ↵ Shrier , I. , A. Szilagyi , and J.A. Correa , Impact of lactose containing foods and the genetics of lactase on diseases: an analytical review of population data . Nutr Cancer , 2008 . 60 ( 3 ): p. 292 – 300 . OpenUrl PubMed 31. ↵ Larsson , S.C. , A.M. Mason , S. Kar , M. Vithayathil , P. Carter , J.A. Baron , K. Michaëlsson , and S. Burgess , Genetically proxied milk consumption and risk of colorectal, bladder, breast, and prostate cancer: a two-sample Mendelian randomization study . BMC Med , 2020 . 18 ( 1 ): p. 370 . OpenUrl 32. ↵ Lumsden , A.L. , A. Mulugeta , and E. Hyppönen , Milk consumption and risk of twelve cancers: A large-scale observational and Mendelian randomisation study . Clin Nutr , 2023 . 42 ( 1 ): p. 1 – 8 . OpenUrl 33. ↵ Schatzkin , A. , C.C. Abnet , A.J. Cross , M. Gunter , R. Pfeiffer , M. Gail , U. Lim , and G. Davey-Smith , Mendelian randomization: how it can--and cannot--help confirm causal relations between nutrition and cancer . Cancer Prev Res (Phila ), 2009 . 2 ( 2 ): p. 104 – 13 . OpenUrl Abstract / FREE Full Text 34. ↵ Newmark , H.L. , M.J. Wargovich , and W.R. Bruce , Colon cancer and dietary fat, phosphate, and calcium: a hypothesis . J Natl Cancer Inst , 1984 . 72 ( 6 ): p. 1323 – 5 . OpenUrl PubMed Web of Science 35. ↵ Prasad , A.R. , S. Prasad , H. Nguyen , A. Facista , C. Lewis , B. Zaitlin , H. Bernstein , and C. Bernstein , Novel diet-related mouse model of colon cancer parallels human colon cancer . World J Gastrointest Oncol , 2014 . 6 ( 7 ): p. 225 – 43 . OpenUrl CrossRef PubMed 36. ↵ Schepens , M.A. , S.J. ten Bruggencate , A.J. Schonewille , R.J. Brummer , R. van der Meer , and I.M. Bovee-Oudenhoven , The protective effect of supplemental calcium on colonic permeability depends on a calcium phosphate-induced increase in luminal buffering capacity . Br J Nutr , 2012 . 107 ( 7 ): p. 950 – 6 . OpenUrl CrossRef PubMed 37. ↵ Fedirko , V. , R.M. Bostick , W.D. Flanders , Q. Long , E. Sidelnikov , A. Shaukat , C.R. Daniel , R.E. Rutherford , and J.J. Woodard , Effects of vitamin d and calcium on proliferation and differentiation in normal colon mucosa: a randomized clinical trial . Cancer Epidemiol Biomarkers Prev , 2009 . 18 ( 11 ): p. 2933 – 41 . OpenUrl Abstract / FREE Full Text 38. ↵ Fedirko , V. , R.M. Bostick , Q. Long , W.D. Flanders , M.L. McCullough , E. Sidelnikov , C.R. Daniel , R.E. Rutherford , and A. Shaukat , Effects of supplemental vitamin D and calcium on oxidative DNA damage marker in normal colorectal mucosa: a randomized clinical trial . Cancer Epidemiol Biomarkers Prev , 2010 . 19 ( 1 ): p. 280 – 91 . OpenUrl Abstract / FREE Full Text 39. ↵ Llor , X. , R.F. Jacoby , B.B. Teng , N.O. Davidson , M.D. Sitrin , and T.A. Brasitus , K-ras mutations in 1,2-dimethylhydrazine-induced colonic tumors: effects of supplemental dietary calcium and vitamin D deficiency . Cancer Res , 1991 . 51 ( 16 ): p. 4305 – 9 . OpenUrl Abstract / FREE Full Text 40. ↵ Tsilidis , K.K. , N. Papadimitriou , N. Dimou , D. Gill , S.J. Lewis , R.M. Martin , N. Murphy , et al. , Genetically predicted circulating concentrations of micronutrients and risk of colorectal cancer among individuals of European descent: a Mendelian randomization study . Am J Clin Nutr , 2021 . 113 ( 6 ): p. 1490 – 1502 . OpenUrl 41. ↵ Karavasiloglou , N. , D.J. Hughes , N. Murphy , L. Schomburg , Q. Sun , V. Seher , S. Rohrmann , et al. , Prediagnostic serum calcium concentrations and risk of colorectal cancer development in 2 large European prospective cohorts . Am J Clin Nutr , 2023 . 117 ( 1 ): p. 33 – 45 . OpenUrl 42. ↵ Peacock , M ., Calcium metabolism in health and disease . Clin J Am Soc Nephrol , 2010 . 5 Suppl 1 : p. S23 – 30 . OpenUrl Abstract / FREE Full Text 43. ↵ Duan , R.D ., Anticancer compounds and sphingolipid metabolism in the colon . In Vivo , 2005 . 19 ( 1 ): p. 293 – 300 . OpenUrl Abstract / FREE Full Text 44. Liew , C. , H.A. Schut , S.F. Chin , M.W. Pariza , and R.H. Dashwood , Protection of conjugated linoleic acids against 2-amino-3-methylimidazo[4,5-f]quinoline-induced colon carcinogenesis in the F344 rat: a study of inhibitory mechanisms . Carcinogenesis , 1995 . 16 ( 12 ): p. 3037 – 43 . OpenUrl CrossRef PubMed Web of Science 45. Park , H.S. , J.H. Ryu , Y.L. Ha , and J.H. Park , Dietary conjugated linoleic acid (CLA) induces apoptosis of colonic mucosa in 1,2-dimethylhydrazine-treated rats: a possible mechanism of the anticarcinogenic effect by CLA . Br J Nutr , 2001 . 86 ( 5 ): p. 549 – 55 . OpenUrl CrossRef PubMed Web of Science 46. Zhang , P. , B. Li , S. Gao , and R.D. Duan , Dietary sphingomyelin inhibits colonic tumorigenesis with an up-regulation of alkaline sphingomyelinase expression in ICR mice . Anticancer Res , 2008 . 28 ( 6a ): p. 3631 – 5 . OpenUrl Abstract / FREE Full Text 47. ↵ Parodi , P.W ., Conjugated linoleic acid and other anticarcinogenic agents of bovine milk fat . J Dairy Sci , 1999 . 82 ( 6 ): p. 1339 – 49 . OpenUrl PubMed Web of Science 48. ↵ Keum , N. , D. Aune , D.C. Greenwood , W. Ju , and E.L. Giovannucci , Calcium intake and colorectal cancer risk: dose-response meta-analysis of prospective observational studies . Int J Cancer , 2014 . 135 ( 8 ): p. 1940 – 8 . OpenUrl CrossRef PubMed 49. ↵ Wactawski-Wende , J. , J.M. Kotchen , G.L. Anderson , A.R. Assaf , R.L. Brunner , M.J. O’Sullivan , K.L. Margolis , et al. , Calcium plus vitamin D supplementation and the risk of colorectal cancer . N Engl J Med , 2006 . 354 ( 7 ): p. 684 – 96 . OpenUrl CrossRef PubMed Web of Science 50. ↵ Thomson , C.A. , A.K. Aragaki , R.L. Prentice , M.L. Stefanick , J.E. Manson , J. Wactawski-Wende , N.B. Watts , et al. , Long-Term Effect of Randomization to Calcium and Vitamin D Supplementation on Health in Older Women: Postintervention Follow-up of a Randomized Clinical Trial . Ann Intern Med , 2024 . 177 ( 4 ): p. 428 – 438 . OpenUrl 51. ↵ Gamage , S.M.K. , L. Dissabandara , A.K. Lam , and V. Gopalan , The role of heme iron molecules derived from red and processed meat in the pathogenesis of colorectal carcinoma . Crit Rev Oncol Hematol , 2018 . 126 : p. 121 – 128 . OpenUrl PubMed 52. ↵ Cross , A.J. and R. Sinha , Meat-related mutagens/carcinogens in the etiology of colorectal cancer . Environ Mol Mutagen , 2004 . 44 ( 1 ): p. 44 – 55 . OpenUrl CrossRef PubMed Web of Science 53. Santarelli , R.L. , F. Pierre , and D.E. Corpet , Processed meat and colorectal cancer: a review of epidemiologic and experimental evidence . Nutr Cancer , 2008 . 60 ( 2 ): p. 131 – 44 . OpenUrl CrossRef PubMed Web of Science 54. ↵ Bouvard , V. , D. Loomis , K.Z. Guyton , Y. Grosse , F.E. Ghissassi , L. Benbrahim-Tallaa , N. Guha , H. Mattock , and K. Straif , Carcinogenicity of consumption of red and processed meat . Lancet Oncol , 2015 . 16 ( 16 ): p. 1599 – 600 . OpenUrl CrossRef PubMed 55. ↵ Watling , C.Z. , R.K. Kelly , N. Murphy , M. Gunter , C. Piernas , K.E. Bradbury , J.A. Schmidt , T.J. Key , and A. Perez-Cornago , Prospective Analysis Reveals Associations between Carbohydrate Intakes, Genetic Predictors of Short-Chain Fatty Acid Synthesis, and Colorectal Cancer Risk . Cancer Res , 2023 . 83 ( 12 ): p. 2066 – 2076 . OpenUrl 56. He , X. , K. Wu , X. Zhang , R. Nishihara , Y. Cao , C.S. Fuchs , E.L. Giovannucci , et al. , Dietary intake of fiber, whole grains and risk of colorectal cancer: An updated analysis according to food sources, tumor location and molecular subtypes in two large US cohorts . Int J Cancer , 2019 . 145 ( 11 ): p. 3040 – 3051 . OpenUrl CrossRef PubMed 57. ↵ Hullings , A.G. , R. Sinha , L.M. Liao , N.D. Freedman , B.I. Graubard , and E. Loftfield , Whole grain and dietary fiber intake and risk of colorectal cancer in the NIH-AARP Diet and Health Study cohort . Am J Clin Nutr , 2020 . 112 ( 3 ): p. 603 – 612 . OpenUrl CrossRef PubMed 58. ↵ Bingham , S.A ., Mechanisms and experimental and epidemiological evidence relating dietary fibre (non-starch polysaccharides) and starch to protection against large bowel cancer . Proc Nutr Soc , 1990 . 49 ( 2 ): p. 153 – 71 . OpenUrl CrossRef PubMed Web of Science 59. ↵ Walker , A.W. , S.H. Duncan , E.C. McWilliam Leitch , M.W. Child , and H.J. Flint , pH and peptide supply can radically alter bacterial populations and short-chain fatty acid ratios within microbial communities from the human colon . Appl Environ Microbiol , 2005 . 71 ( 7 ): p. 3692 – 700 . OpenUrl Abstract / FREE Full Text 60. ↵ Mirvish , S.S ., Effects of vitamins C and E on N-nitroso compound formation, carcinogenesis, and cancer . Cancer , 1986 . 58 ( 8 Suppl): p. 1842-50 . OpenUrl 61. ↵ Leenders , M. , A.M. Leufkens , P.D. Siersema , F.J. van Duijnhoven , A. Vrieling , P.J. Hulshof , C.H. van Gils , et al. , Plasma and dietary carotenoids and vitamins A, C and E and risk of colon and rectal cancer in the European Prospective Investigation into Cancer and Nutrition . Int J Cancer , 2014 . 135 ( 12 ): p. 2930 – 9 . OpenUrl CrossRef PubMed 62. ↵ VanderWeele , T.J. , Outcome-wide Epidemiology. Epidemiology , 2017 . 28 ( 3 ): p. 399 – 402 . OpenUrl 63. ↵ Greenwood , D.C. , L.J. Hardie , G.S. Frost , N.A. Alwan , K.E. Bradbury , M. Carter , P. Elliott , et al. , Validation of the Oxford WebQ Online 24-Hour Dietary Questionnaire Using Biomarkers . Am J Epidemiol , 2019 . 188 ( 10 ): p. 1858 – 1867 . OpenUrl CrossRef PubMed View the discussion thread. Back to top Previous Next Posted June 13, 2024. Download PDF Supplementary Material Data/Code Email Thank you for your interest in spreading the word about medRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. You are going to email the following Diet-wide analyses for risk of colorectal cancer: prospective study of 12,250 incident cases among 543,000 women in the UK Message Subject (Your Name) has forwarded a page to you from medRxiv Message Body (Your Name) thought you would like to see this page from the medRxiv website. 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