P < 0.05). Genome-wide average DNA methylation was negatively associated with smoking and number of live births, and positively associated with age at first live birth . The association of DNAm age with BMI was also evident in within-twin-pair analyses that control for familial factors. This study suggests that some lifestyle and hormonal risk factors are associated with these DNA methylation-based breast cancer risk factors, and the observed associations are unlikely to be due to familial confounding but are likely causal. DNA methylation-based risk factors could interplay with conventional risk factors in modifying breast cancer risk.DNA methylation-based biological age (DNAm age), as well as genome-wide average DNA methylation, have been reported to predict breast cancer risk. We aimed to investigate the associations between these DNA methylation-based risk factors and 18 conventional breast cancer risk factors for disease-free women. A sample of 479 individuals from the Australian Mammographic Density Twins and Sisters was used for discovery, a sample of 3354 individuals from the Melbourne Collaborative Cohort Study was used for replication, and meta-analyses pooling results from the two studies were conducted. DNAm age based on three epigenetic clocks  and genome-wide average DNA methylation were calculated using the HumanMethylation 450 K BeadChip assay data. The DNAm age measures were positively associated with body mass index (BMI), smoking, alcohol drinking and age at menarche (all nominal  DNA methylation, especially when detected in blood, has been investigated for its association with breast cancer risk. Peripheral blood DNA methylation at the known breast cancer susceptibility genes BRCA1 and ATM has been found to be associated with elevated breast cancer risk5. Several genome-wide studies of DNA methylation have reported peripheral blood DNA methylation changes associated with the risk of breast cancer10.DNA methylation has been proposed to play an important role in the aetiology of complex traits and diseases, including cancer11, and the Hannum12 and Horvath13 clocks have received the most attention. Both these clocks are developed by regressing chronological age on individual methylation sites to select a set of sites to predict chronological age. The Levine clock14 has recently been developed by selecting a set of sites to predict ‘phenotypic age’, a linear combination of 10 clinical biomarkers  associated with the hazard of aging-related mortality. The difference between DNAm age and chronological age is called ‘epigenetic age acceleration’, which reflects the rate of biological aging. A positive value of epigenetic age acceleration suggests that biological age is older than chronological age. Prospective analyses have found associations between accelerated DNAm ages based on the Horvath and Levine clocks and increased risk of breast cancer for middle-aged women15. Recently, a prospective study assessed DNAm age for 2,764 middle-aged women using DNA extracted from pre-diagnosis blood samples and found that each of the three clocks above was predictive of breast cancer risk: each 5-year increase in epigenetic age acceleration  was associated with about a 10–15% increase in breast cancer risk16.Two DNA methylation-based measures have been reported to be associated with breast cancer risk. One measure is DNA methylation-based biological age (DNAm age). Several epigenetic clocks have been developed to estimate DNAm age18. Two studies found that a lower genome-wide average DNA methylation in pre-diagnosis peripheral blood was associated with increased risk of breast cancer9. These results are consistent with previous findings that conventional global blood DNA methylation measures, such as LINE-1 and LUMA, are negatively associated with breast cancer risk20. However, this association was not observed in the Norwegian Women and Cancer Study9, the Sister Study10 or a recent meta-analysis of four studies21, two of which are the studies of Refs. 8 and 9. A similar measure, the median methylation value across probes, has been found to be prospectively associated with risks of mature B-cell neoplasms22, urothelial cell carcinoma23 and prostate cancer24.Another DNA methylation measure reported to be associated with breast cancer risk is a global measure, genome-wide average DNA methylation, defined as the average methylation value across multiple probes, commonly calculated from the HumanMethylation 450 K (HM450) BeadChip25: “in-depth studies of both established and putative breast cancer risk factors for their relationship with epigenetic age acceleration can add to our understanding of the biology underlying disease risk factors and present new opportunities for primary and secondary prevention of breast cancer.”These measures could be treated as putative DNA methylation-based breast cancer risk factors. It is unknown whether these DNA-methylation based risk factors and conventional risk factors modify breast cancer risk independently or in combination. Knowing the associations between the two groups of risk factors could be helpful for answering this question and provide insights into breast cancer aetiology and risk prediction. As Christensen pointed out11. Levine et al.26 found age at menopause was negatively associated with accelerated DNAm age based on the Horvath clock, opposite to the direction of the two factors’ associations with breast cancer risk. From a twin and family study, we found evidence that the variance in genome-wide average DNA methylation is determined by (unmeasured) environmental factors across the lifespan17; no specific environmental factor was investigated. To the best of our knowledge, associations of these DNA methylation-based risk factors with conventional breast cancer risk factors, such as family history or mammographic density, have not been investigated.DNAm age has been investigated for its associations with lifestyle factors and some inconsistent results were reportedWe therefore conducted a study with the aim of investigating associations between DNA methylation-based risk factors and conventional risk factors for breast cancer.15 that reported on the DNA methylation-based breast cancer risk factors considered in this study.Table The mean (SD) DNAm age based on the Hannum, Horvath and Levine clocks were 57.3 (6.4), 55.5 (6.5) and 53.0 (7.4) years, respectively. By construction, the mean epigenetic age acceleration was zero years, and the SD was 4.2, 4.7 and 5.6 years, respectively. The mean (SD) percentage genome-wide average DNA methylation was 53.0% (0.3%).P < 10−15). The three epigenetic age acceleration measures were correlated with each other , and independent of chronological age by construction. Genome-wide average DNA methylation was independent of chronological age, DNAm age and epigenetic age acceleration . No obvious heterogeneity in the correlations across MZ twins, DZ twins and sisters was observed.Figures P < 0.05); BMI was the only risk factor associated with two epigenetic acceleration measures. Genome-wide average DNA methylation was negatively associated with the number of live births, and positively associated with age at first live birth . No association was found for the other risk factors.Table P = 0.04 for overweight women, and β = 1.12, 95% CI = 0.08 to 2.17, P = 0.04 for obese women. As to the Levine clock, the association for obese women was marginally significant: β = 1.29, 95% CI = −0.01 to 2.58, P = 0.05. No association was found for the Hannum clock or genome-wide average DNA methylation. The analyses including the whole sample for pack-years of smoking, number of live births, length of HRT use and length of oral contraceptive use gave similar results (data not shown).Similar results were found from the sensitivity analyses. For categorized BMI, positive associations were found for the Horvath clock: regression coefficient (β) = 1.10, 95% confidence interval (CI) = 0.03 to 2.17, P < 0.05), the association between BMI and the Levine clock was replicated using both the MCCS women and men, and the association between pack-years of smoking and the Levine clock was replicated using the MCCS men.The associated risk factors found above were examined using the Melbourne Collaborative Cohort Study (MCCS) sample Table . At the P = 0.07). Smoking status was associated with the greatest number of DNA methylation-based risk factors investigated: the Horvath clock, Levine clock and genome-wide average DNA methylation.The associations for the Hannum clock were not statistically significant from any of the meta-analyses, and the rest of the associations were found to be nominally significant in at least one of the meta-analyses, with the exception that the association between number of live births and genome-wide average DNA methylation was marginally significant (P = 0.04). Note that this association was also significant in the meta-analysis when including women only.No evidence of heterogeneity by sex was found for any association, except for the association between smoking status and the Horvath clock (P > 0.05).The associated risk factors found above were examined using within-twin-pair analyses Table . For all13 which have reported that DNAm age and genome-wide average DNA methylation are predictive of breast cancer risk, we investigated the associations between these DNA methylation-based risk factors and multiple conventional breast cancer risk factors. We further conducted replication and meta-analyses using independent and homogenous samples, as well as within-twin-pair analyses that control for familial confounding. We found that DNAm age was associated with lifestyle factors  and hormonal factors (age at menarche), while genome-wide average DNA methylation was associated with hormonal factors (number of live births and age at first live birth) and smoking. We also found evidence that the association of DNAm age with BMI remained after controlling for familial factors shared by twins.Using a sample of unaffected women comparable with those in the previous studies17, here we found that smoking, number of live births and age at first live birth were associated with this summary measure of DNA methylation across the genome. To the best of our knowledge, our study is the first to report that genome-wide average DNA methylation is associated with these three conventional risk factors.Following our previous finding that the variance in genome-wide average DNA methylation is determined to a large extent by as yet unmeasured environmental factors across the human lifespan, including non-genetic factors shared by relatives30, we also found DNAm age was positively associated with lifestyle risk factors including BMI, smoking and alcohol drinking. This suggests that some lifestyle factors accelerate biological ageing, consistent with both DNAm age and those lifestyle factors being associated with an increased risk of breast cancer. We found that the Levine clock was associated with more lifestyle factors than the other two clocks. Similar findings have arisen from previous studies; see Table et al.31 Interestingly, the Levine clock was found to be most strongly associated with breast cancer risk16. These differences might be due to differences in the development of these clocks; the Horvath and Hannum clocks are trained on chronological age13, while the Levine clock is trained on the ‘phenotypic age’ which in addition to chronological age also considers nine other health-related factors, so is theoretically more relevant to health14.Similar to previous studies32. Binder and colleagues found a negative association between the Horvath clock and age at menarche32, which is in the opposite direction of our observed association. One difference between the two studies is that Binder et al. studied adolescent girls while we studied older women. Note that the point estimates for the associations of age at menarche and the other two clocks were also positive, providing some more evidence about the direction of association between DNAm age and age at menarche for older women.We found that DNAm age was associated with hormonal risk factors, as has been previously foundResults from the within-twin-pair analyses suggest that our observed association of the Horvath clock with BMI is unlikely to be due to confounding due to familial factors shared by twins, because such potentially confounding effects are cancelled out when using a regression analysis of within-twin-pair differences. In this sense, this association is more likely to be causal. We did not find evidence for genetic confounders, given that the within-twin-pair association was similar for MZ and DZ twin pairs. The null results for the other within-twin-pair associations do not necessarily imply that the associations observed in Tables Our findings imply that DNA methylation-based risk factors potentially interplay with their associated conventional risk factors in modifying breast cancer risk. Such interplay may be the due to the DNA methylation-based risk factors mediating the associations between conventional risk factors and cancer risk, or other mechanisms. Studies with appropriate data, design and analytic methods are required to further investigate the interrelationships between DNA methylation-based risk factors, conventional risk factors and breast cancer risk. Findings from such studies could provide novel insights on the aetiology, early detection and prevention of breast cancer.The strengths of our study include: (1) using samples comparable with those used in studies that reported the DNA methylation-based risk factors, (2) investigating a comprehensive list of conventional breast cancer risk factors, which notably includes family history and mammographic density, and (3) using a sample for discovery and homogenous samples for replication analyses, as well as conducting a meta-analysis to take advantage of the increased sample size. One potential limitation is that no multiple testing adjustment was performed; our results must of course be interpreted with this in mind.In conclusion, our study found evidence that the lifestyle risk factors BMI, smoking and alcohol drinking, and the hormonal breast cancer risk factors age at menarche, age at first live birth and number of liver births, are associated with the DNA methylation-based biological age and global DNA methylation. We also found evidence that the observed associations are unlikely to be due to familial confounding but are likely causal. DNA methylation-based risk factors could interplay with conventional risk factors to modify breast cancer risk. Such interplay requires further investigation.33, which was approved by Human Research Ethics Committee at the University of Melbourne in accordance with the Declaration of Helsinki. All participants provided written informed consent. The analytical sample included 479 women from 130 families: 66 monozygotic twin (MZ) pairs, 66 dizygotic twin (DZ) pairs and 215 sisters of twins34. They were aged from 40–78 years and had a mean (standard deviation [SD]) age of 55.7 (8.0) years. All women were healthy and none had been diagnosed with breast cancer when recruited.The sample was participants from the Australian Mammographic Density Twins and Sisters Study (AMDTSS)35 for type I and II probe bias correction, and an empirical Bayes batch effects removal method, ComBat36, was applied to minimise technical variation across physical batches; see Li et al.34 for more details.Methylation of DNA extracted from dried peripheral blood spots was measured using the HM450 assay. Data were normalized using Illumina’s reference factor-based normalization methods and subset-quantile within array normalizationhttps://dnamage.genetics.ucla.edu). As done previously16, we investigated epigenetic age acceleration, calculated as the residuals from regressing DNAm age on chronological age and blood cell composition. Therefore, epigenetic age acceleration was independent of chronological age and blood cell composition. Blood cell composition  was estimated using the Houseman method37 implemented in the minfi package and Reinius et al.38 as reference. Genome-wide average DNA methylation was calculated as the average methylation beta-value across all autosomal probes that passed quality control.DNAm age was calculated based on each of the Hannum, Horvath and Levine clocks, using the online calculator  with the conventional risk factors separately using a linear regression model, in which the DNA methylation-based risk factor was the dependent variable and the conventional risk factor was the independent variable. To account for the relatedness between family members, the regression model was fitted using the Generalised Estimating Equations method with family as cluster. The model was adjusted for age, BMI, smoking status and blood cell composition, with the exception that blood cell composition was dropped from the model investigating epigenetic age acceleration, since epigenetic age acceleration is independent of cell composition by construction. A Mammographic dense, non-dense and percentage dense areas were power-transformed with powers of 0.25, 0.25, 0.4, respectively, and the other right-skewed continuous risk factors  were log transformed, to have an approximately normal distribution. The analyses for pack-years of smoking, age at menopause, number of live births, age at first live birth, length of HRT use and length of oral contraceptive use, were restricted to participants who were ever smokers, post-menopausal, parous, ever HRT users and ever oral contraceptive users, as appropriate.BMI was analysed as a categorical variable: normal , overweight  and obese . The analyses for pack-years of smoking, number of live births, length of HRT use and length of oral contraceptive use included non-smokers, nulliparous women, HRT non-users and oral contraceptive non-users as well, for whom the corresponding variable was treated as zero and added by one in log transformation.P < 0.05, we performed replication analyses using the data of participants  from the Melbourne Collaborative Cohort Study (MCCS)39. The participants were the controls from case-control studies of several cancers nested in the MCCS. Same as those in the AMDTSS, DNA samples were extracted from peripheral blood, DNA methylation data were measured using the HM450 assay and normalized using Illumina’s reference factor-based normalization methods and subset-quantile within array normalization; see Dugue et al.27 for more details.For the associations with a nominal The association was investigated using the same linear regression model. Additional to the covariates used in the AMDTSS analysis, country of birth  and sample type  were adjusted for, all fitted as fixed-effects. Batch effects were minimized by fitting study and the plate on which the sample was processed as random effects. We also investigated the associations between smoking status and DNA methylation-based risk factors. The analysis was stratified by sex. The heterogeneity in the association by sex was investigated by fitting an interaction term of the risk factor with sex in the regression model for the whole sample.40, results from the two studies were pooled via a fixed-effect meta-analysis, using the generic inverse variance method. Two meta-analyses were performed: (1) AMDTSS sample and MCCS women, and (2) AMDTSS sample, MCCS women and MCCS men.Taking advantage of the homogeneity between the two samples41. The within-twin-pair analyses were performed separately for all twin pairs, MZ pairs and DZ pairs. We investigated the heterogeneity in the within-twin-pair association by zygosity by fitting an interaction term of the risk factor with zygosity in the regression model for all twin pairs.Taking advantage of our sample including twin pairs, we performed within-twin-pair analyses to investigate the associations after controlling for the confounding effects of familial factors (both known and unknown) shared by twins. Within-twin-pair differences in the DNA methylation-based risk factors, conventional risk factors and covariates were calculated, and used to estimate their within-twin-pair associations using an ordinary linear regression model without an intercept


Is there an association between specific subtypes of circulating leukocytes and the risk of breast cancer?In this case-cohort study of 2774 women, lower proportions of circulating monocytes were associated with a higher risk of breast cancer within 1 year of the blood collection, whereas higher proportions of circulating B cells were associated with a higher risk of breast cancer 4 or more years later.Shifts in circulating leukocyte profiles appear to precede a breast cancer diagnosis and may serve as markers of time-dependent breast cancer risk. This case-cohort study examines the associations between proportions of circulating leukocyte subtypes and the risk of breast cancer. Higher overall leukocyte counts in women may be associated with increased risk of breast cancer, but the association of specific leukocyte subtypes with breast cancer risk remains unknown.To determine associations between circulating leukocyte subtypes and risk of breast cancer.Between 2003 and 2009, the Sister Study enrolled 50 884 women who had a sister previously diagnosed with breast cancer but were themselves breast cancer free. A case-cohort subsample was selected in July 2014 from the full Sister Study cohort. Blood samples were obtained at baseline, and women were followed up through October 2016. Data analysis was performed in April 2019.+ and CD4+ T cells, monocytes, and granulocytes). Leukocyte subtype proportions were dichotomized at their population median value, and Cox proportional hazard models were used to estimate associations with breast cancer.The main outcome was the development of breast cancer in women. Whole-blood DNA methylation was measured, and methylation values were deconvoluted using the Houseman method to estimate proportions of 6 leukocyte subtypes . Among women who were premenopausal at blood collection, the association between B cells and breast cancer was significant , and an inverse association for circulating proportions of monocytes was found . Among all women, associations between leukocyte subtypes and breast cancer were time dependent: higher monocyte proportions were associated with decreased near-term risk , whereas higher B cell proportions were associated with increased risk 4 or more years after blood collection .Among 2774 non-Hispanic white women included in the analysis , 1295 women were randomly selected from the full cohort (of whom 91 developed breast cancer) along with an additional 1479 women who developed breast cancer during follow-up . Circulating proportions of B cells were positively associated with later breast cancer (hazard ratio [HR], 1.17; 95% CI, 1.01-1.36; Circulating leukocyte profiles may be altered before clinical diagnoses of breast cancer and may be time-dependent markers for breast cancer risk, particularly among premenopausal women. Although immunosurveillance provides an important first defense against neoplastic cells, immune responses can also be associated with tumor growth by altering tissue microenvironments and selecting more-virulent cells through immunoediting.13 suggest that women with higher overall leukocyte counts, or certain autoimmune disorders, may be at increased risk of breast cancer. However, leukocytes are diverse and can be broadly defined by their cell lineage (myeloid and lymphoid); these subtypes may be differentially associated with breast cancer. For example, increasing proportions of myeloid-lineage subtypes have been reported to contribute to tumor development.16 Few large-scale studies have assessed immune cell profiles; isolation of leukocyte subtypes in peripheral blood has traditionally required flow cytometry, which is an expensive and time-consuming assay that requires fresh blood samples.17 Thus, studies of breast cancer and leukocyte subtypes have been limited to small case-control studies20 using case samples obtained at diagnosis.Prospective epidemiologic studies26 Epigenome-wide methylation array data are increasingly available in large epidemiologic studies and can be deconvoluted to estimate proportions of common leukocyte subtypes.22 Using such deconvolution, methylation-derived leukocyte profiles, including the methylation-derived neutrophil-to-lymphocyte ratio (mdNLR), have been shown to be altered in patients with a variety of cancers and appear to be associated with cancer prognosis.33 Recently, among a small population of heavy smokers, the mdNLR was reported to be altered years before a lung cancer diagnosis, supporting the hypothesis that circulating leukocyte profiles may be markers of cancer risk.34 Here, we use prospectively collected peripheral blood samples from a large, nationwide cohort of women and report associations between methylation-derived leukocyte profiles and breast cancer risk.Leukocyte subtypes can be distinguished by their lineage-specific patterns of DNA methylation, allowing estimation of circulating leukocyte composition using blood DNA.35 enrolled a cohort of 50 884 women residing in the United States or Puerto Rico between 2003 and 2009. Eligible women were aged 35 to 75 years and could not have had breast cancer themselves but must have had a biological sister who received a diagnosis of breast cancer. Enrolled women are recontacted annually by email, mail, or telephone and are asked to complete either a short questionnaire about recent diagnoses, including any cancer diagnosis, or a more comprehensive questionnaire (every third year) on changes in health, lifestyle, and exposures. The annual response rate has consistently been greater than 90%. Women reporting a breast cancer diagnosis are contacted 6 months after diagnosis and asked to provide authorization to retrieve medical records.The Sister Study36 To avoid possible confounding by ancestry, our analysis was restricted to white women who self-reported as non-Hispanic. For the subcohort, approximately 3% (1336) of eligible non-Hispanic white women were randomly selected from the full cohort . The random subcohort is meant to be representative of all non-Hispanic white women in the cohort. Of these 1336 women, 91 received a diagnosis of breast cancer after enrollment but before October 2016 (data release 6.0). As our case set, we selected the remaining eligible 1540 non-Hispanic white women who received a diagnosis of ductal carcinoma in situ (DCIS) or invasive breast cancer during the time between enrollment and when the case-cohort was sampled (July 2014). Person-time of the women selected into the case-cohort was weighted back to the population of non-Hispanic white women from the full Sister Study cohort.37 Data analysis was conducted in April 2019.A case-cohort subsample of women was selected for whole-blood DNA methylation analysis  reporting guideline.Written informed consent and blood samples were obtained during a home visit at enrollment. The study was approved by the institutional review boards of the National Institute of Environmental Health Sciences and the Copernicus Group. This study follows the Strengthening the Reporting of Observational Studies in Epidemiology , were selected for analysis .Although the stability of DNA methylation at individual CpG sites included on the HumanMethylation450 BeadChip has been examined,42 In brief, DNA was extracted from whole blood, and 1 μg was bisulfite-converted in 96-well plates using the EZ DNA Methylation Kit (Zymo Research). Samples were tested for completion of bisulfite conversion and, according to the manufacturer’s protocol, analyzed on Infinium HumanMethylation450 BeadChips (Illumina). For the independent sample used to examine the stability of the distribution of leukocyte subtypes over time, methylation was measured using an older microarray, the Infinium HumanMethylation27 BeadChip (Illumina). Methylation analysis was performed at the National Institutes of Health Center for Inherited Disease Research. The arrays were processed using high-throughput robotics to minimize batch effects. Methylation data preprocessing and quality control were completed using the ENmix method43 in R statistical software version 3.6.1 . This included reducing background noise with the ENmix method, correcting fluorescent dye bias using the RELIC method,44 quantile normalization to make overall array fluorescence intensity distribution comparable between arrays, and reducing Infinium I and II probe design bias using the regression on correlated probes method.45 We excluded 102 samples after quality control (61 cases and 41 noncases). Of these 102 samples, 91 had mean bisulfite intensity less than 4000 or had greater than 5% of probes with low-quality methylation values , 4 were outliers for their methylation beta value distributions, 1 had missing phenotype data, and 6 were from women whose date of diagnosis preceded blood collection.46 After excluding women whose blood methylation data failed quality control, there remained 1295 women from the random subcohort (of whom 91 developed breast cancer by October 2016) and 1479 women from the case set. Of the 1570 women with incident DCIS or invasive breast cancer who were included, pathology reports were obtained for 1539 (98%) of these women. Among women for whom we obtained pathology reports, the positive predictive value of a self-reported breast cancer was 99.4%.47The DNA processing procedures have been described elsewhere.+ and CD4+ T cells (lymphoid lineage), and monocytes and granulocytes (myeloid lineage).21 This deconvolution method has been validated using DNA methylation, and complete blood cell count data were assessed for the same blood collection.22 For each woman, the estimated proportions of the 6 cell types sum to 1. The mdNLR was calculated by dividing the estimated proportion of granulocytes by the sum of the lymphocyte proportions .34 The leukocyte proportions and mdNLR estimates were then dichotomized at the median values for women in the random subcohort.We used the Houseman method to statistically deconvolute distributions of 6 leukocyte subtypes: B cells, natural killer cells, and CD8t tests for continuous and ordinal variables and χ2 tests for categorical variables. We further examined Spearman correlation coefficients (sr) between the leukocyte proportions. To test the stability of the distribution of leukocyte subtypes, intraclass correlation coefficients (ICCs) were calculated using 2-way mixed-effect models,48 treating the participant as a random effect and the visit at which the blood sample was collected as a fixed effect. To measure associations between the leukocyte subtypes and breast cancer risk, we used case-cohort Cox proportional hazard models with robust standard errors to calculate hazard ratios (HRs), 95% CIs, and 2-sided P values, with P ≤ .05 considered statistically significant.37 We excluded 1 DCIS case for whom age was missing at diagnosis. Because age was treated as the primary timescale for this analysis,49 all resulting HRs are, therefore, fully adjusted for age. In our primary analysis, we combined DCIS and invasive breast cancer diagnoses to represent all breast cancers. Menopause could affect leukocyte composition50; we therefore examined associations for women both overall and stratified by menopause status at blood collection and statistically tested for heterogeneity using an interaction term of the leukocyte subtype by menopause status. In follow-up analyses, we separately examined risks for DCIS and invasive breast cancer, treating the breast cancer subtype of interest as an event and otherwise censoring women at the time of their diagnosis or the end of follow-up (October 2016). We explored time dependence by examining whether leukocyte associations with breast cancer varied by the number of years between the blood collection and clinical diagnosis of breast cancer. Specifically, we estimated associations with breast cancer risk within 1 year of blood collection, 1 to less than 4 years after blood collection, and 4 years or more after blood collection.Comparing women who developed breast cancer with those who remained cancer free through follow-up, we described characteristics using means and SDs or counts and proportions. We tested for differences in baseline characteristics using 2-sample 55 The association between body mass index and breast cancer varies by menopause status52; we therefore included an interaction term between menopause and body mass index. Analyses were conducted using Stata statistical software version 15 (StataCorp).We examined potential confounding by adjusting for established breast cancer risk factors measured at baseline, including menopause status (premenopause vs postmenopause), continuous versions of body mass index , physical activity, current alcohol intake, number of live births, age at first birth (among parous women), age at menarche, breastfeeding duration, and duration of hormone therapy and oral contraception use.By design, all 2774 women sampled were non-Hispanic white . The mean (SD) duration of follow-up was 6.0 (3.2) years. The women who developed breast cancer over follow-up tended to be older at blood collection , engage in less physical activity , and be older at menopause onset  . The mea+ T cells were the smallest percentage   and were inversely correlated with all lymphoid lineage leukocytes .As expected, among women randomly selected from the full cohort, granulocytes composed the largest percentage of circulating leukocytes  and CD8 [3.0%]) . Myeloidukocytes . The mossr = 0.25; 95% CI, 0.20 to 0.30; P < .001) and inverse correlations with CD8+ T cells  and granulocytes  , CD4+ T cells , or monocytes .Age had a positive correlation with natural killer cells  (P = .70), CD8+ T cells , and CD4+ T cells  greater than the subcohort median had higher breast cancer risks, but the associations were not statistically significant. A higher mdNLR was not associated with breast cancer risk , nor were increases in monocytes  or granulocytes . Model adjustment for baseline covariates did not meaningfully alter the HR estimates; therefore, we focus on models without covariates (model 1) , whereas higher proportions of monocytes were associated with decreased breast cancer risk among premenopausal women   . Women wmodel 1) . After smodel 1) . SpecifiP = .01) (P = .001). After stratification by tumor invasiveness, the year 1 association with monocyte proportions appeared to be more significant among women with invasive tumors  (eTable 3 in the P = .02)  . Women w+ and CD4+ T cells had no associations overall or after stratification by menopause status, the proportions appeared to be associated with breast cancer within 1 year of blood collection  that are often found in the tumor microenvironment,75 may provide additional insights into the interplay of the immune system and breast cancer risk. Our study also includes multiple comparisons, and we did not explicitly adjust P values for the number of statistical tests. In addition, although we assessed potential confounding by established breast cancer risk factors that we measured, there is still the opportunity for residual confounding by mammographic density, medication use, or other unmeasured covariates.This study has limitations. Although we found time-dependent shifts in leukocyte profiles before a breast cancer diagnosis, our study was observational and we could only assess the relative proportions of leukocytes at the time when follow-up began. Thus, we could only describe associations between leukocyte subtypes and later breast cancer risk and could not assess effects of leukocyte changes or identify mechanisms. Although blood DNA methylation can be used to estimate the proportions of different leukocyte subtypes, we have no information on overall leukocyte counts. In our study, leukocyte subtypes were estimated using the Illumina HumanMethylation450 platform, which has been validated using external populations and is reported to have good reliability.In summary, we found that the relative proportions of circulating leukocyte subtypes appeared to be associated with the risk of breast cancer months to years after blood collection. Although routine clinical white blood cell counts do not differentiate among lymphocyte subtypes, monocyte differential counts could be obtained from medical records and may be associated with near-term breast cancer risk. Moreover, in epidemiologic settings, methylation-based deconvolution provides a useful tool for high-throughput cellular phenotyping and may offer the opportunity to test immunologic hypotheses in large populations.


We included 1852 colorectal cancer and 1567 breast cancer cases, and 3447 and 1486 population controls, respectively. DII score and NEAC were derived using data from a semi-quantitative validated food frequency questionnaire. Unconditional logistic regression models were used to estimate odds ratios (OR) and 95% confidence intervals (95%CI) for energy-adjusted DII (E-DII), and a score combining E-DII and NEAC. E-DII was associated with colorectal cancer risk ; this increase was observed for both colon and rectal cancer. Less pronounced increased risks were observed for breast cancer . The combined score of high E-DII scores and low antioxidant values were associated with colorectal cancer risk , but not breast cancer. This study provides evidence that a pro-inflammatory diet is associated with increased colorectal cancer risk while findings for breast cancer were less consistent.Inflammation and antioxidant capacity have been associated with colorectal and breast cancer. We computed the dietary inflammatory index (DII Colorectal cancer (CRC) is the third most common cancer worldwide . Beyond Among females, breast cancer (BC) and CRC are the two most frequently diagnosed cancers ,7. Age, Despite that CRC and BC are different cancers affecting different organs, they share similar risk factors. Scientific evidence has shown that chronic inflammation and oxidative stress predispose the pathogenesis of numerous diseases, including CRC and BC ,11,12,13®), which was specifically designed to assess and offer quantitative information about the inflammatory potential of the diet [Diet may play a role in the regulation of chronic inflammation, as shown by the relation between dietary factors and blood levels of inflammatory markers ,17,18. Tthe diet . The DIIthe diet ,21,22 inthe diet ,24,25. Tthe diet . The nonthe diet ,28. Numerous epidemiological studies have assessed the association between DII and energy-adjusted (E-DII) scores and CRC and BC risk, supporting the hypothesis that a pro-inflammatory diet is related to CRC risk . The eviThus far, studies essentially have focused on assessing the role of the inflammatory potential of the diet on cancer risk, without considering the plausible combined effect of a global indicator of NEAC. Using the newly constructed E-DII in the population-based multi-case-control (MCC)-Spain study our aim is to analyze the association between the inflammatory potential of the diet, and the risk of developing BC and CRC, as well as among tumor subtypes. A second aim of the present study is to elucidate the relation between E-DII and NEAC, and to evaluate whether having an anti-inflammatory and antioxidant diet affects the risk of developing CRC and BC.MCC-Spain is a population-based multicenter case-control study designed to investigate the etiology of the principal cancer sites  in adults. The study design and protocol have been described in detail elsewhere . The MCCThe CRC sub-study included 1852 cases and 3347 controls, and the BC sub-study included 1486 cases and 1652 controls . Newly dhttp://www.mccspain.org. Face-to-face interviews were carried out at baseline by trained personnel using a structured computerized epidemiological questionnaire to assess data on socio-demographic, lifestyle, environmental exposure, residential history, personal/family medical history, drug use, and weight information at different ages. The questionnaire is available at Information on dietary data was assessed using a self-administered semi-quantitative food frequency questionnaire (FFQ), which was an adapted version of a Spanish-validated FFQ  as it waThe assessment of the dietary NEAC in the MCC-Study has been previously described in detail . BrieflyDII scores were calculated using a method previously reported by Shivappa et al. . Brieflyt-test for continuous variables normally distributed, Wilcoxon rank-sum test for continuous variables non-normally distributed, and Pearson χ² test for categorical variables. The E-DII was analyzed as a continuous variable (one-unit increment) and as a categorical variable, expressed as quartiles based on the sex-specific distribution in the control group (CRC or BC controls). The first E-DII quartile was treated as reference category . Baseline dietary and sociodemographic characteristics were examined using means and standard deviations (SD) for continuous variables and percentages for categorical variables by CRC and BC cases and controls, and across E-DII quartiles. Differences between cases and controls were assessed using Student’s The odds ratios (OR) and the corresponding 95% confidence intervals (95%CI) for the association between E-DII and CRC and BC risk were estimated using unconditional logistic regression models. Analyses also were conducted separately by CRC anatomic subsites (colon and rectum) and BC subtypes . Tests for dose-response trend were estimated by including the exposure variable as continuous ordinal (scored from 1 to 4) in the regression model. 2), and smoking status  was evaluated by including interaction terms and by stratified analysis. The models with and without the interaction terms were compared using the likelihood ratio test (LRT). Effect-measure modification by NSAIDs , physical activity , BMI  one year before recruitment, leisure-time physical activity calculated for the last 10 years of life, excluding the last 2 years previous to the interview , and non-steroidal anti-inflammatory drugs use .For analyses where CRC was the main outcome, two different models are presented: (1) A simple model adjusting for age, sex, study area, and educational level ; (2) a final model, additionally adjusting for family history as first-degree relative of CRC , smoking status , BMI  A simple model ; (2) a final model further adjusted by family history first degree of BC , smoking status, BMI, physical activity, hormonal replacement therapy use , oral contraceptive use , age at menarche , age at first pregnancy , number of children, and menopausal status .Potential effect-measure modification of the association between E-DII and BC risk also was assessed using a LRT in the mutually adjusted model by physical activity, BMI, smoking status, menopausal status , HRT use , and OC use .Three sensitivity analyses for both CRC and BC final models were performed: 1) Excluding those participants who had more than 6 months between the data of diagnosis and the interview ; 2) further adjusting the final CRC and BC models by energy intake from non-alcohol sources and alcohol consumption; 3) restricting the analyses to non-drinkers. To assess the second aim of the present study, two approaches were conducted. First, an inflammatory and antioxidant profile was designed, and it was used as the main exposure variable in both CRC and BC final models. A value of 0, 1, 2, and 3 was assigned to the first, second, third, and fourth quartile of E-DII , whereas a value of 3, 2, 1, and 0 was assigned to the first, second, third, and fourth quartile of NEAC . When CRC was evaluated, quartiles values were sex-specific. For each participant, the values received were summed to assess the profile score. The profile ranged from 0 (less inflammatory and high antioxidant) to 6 (high inflammatory and less antioxidant). Linear trend tests were used to calculate the OR for the profile as a continuous variable (1 point-increment). Second, a combined categorical variable including both indicators (E-DII and TEAC) was created using median values (sex-specific for CRC). This four-category variable was used as the main exposure variable in final models. Those participants classified having high antioxidant and low inflammatory potential of diet were selected as the reference category. Potential effect-measure modification of the association between E-DII+NEAC score and both cancer outcomes was evaluated by vegetable intake , all meat intake , and fiber intake .p < 0.05.Statistical analyses were conducted using R: A language and environment for statistical computing, version 3.5 . All statistical tests were two-sided and statistical significance was set at p < 0.001). BC cases compared to controls also had higher E-DII scores, tended to be younger, and reported more frequently being smokers, premenopausal, and having a first-degree history of BC (p < 0.001). Similar distributions were observed among BC cases and controls regarding HRT, OC use, and age at first pregnancy (p > 0.05). The present study included 1852 CRC and 1486 BC cases, and 3447 and 1652 population controls, respectively. The main characteristics of cases and controls are presented in p < 0.001). Among female controls, those classified in the Q4 compared with Q1 tended to be premenopausal and were OC but not HRT users. No differences were observed across quartiles regarding age at menarche, age at first pregnancy, and number of children .Characteristics of participants in the control group across quartiles of E-DII are shown in E-DII: 1.15, 95%CI: 1.11–1.19) and fully adjusted models . Participants classified at the fourth E-DII quartile were at higher risk of developing CRC . This association was consistent when colon and rectal CRC subtypes were evaluated using the continuous E-DII variable per 1-point increment . Results remained unchanged when we performed the three sensitivity analyses: (1) Excluding those participants who had more than six months between the data of diagnosis and the interview  adjp-value for trend: <0.001; LRT p-value: 0.04), but higher ORs were observed in men  than in women   .p-values showed evidence for heterogeneity .Effect-measure modification by BMI, physical activity, NSAIDs/aspirin use, and tobacco smoking was evaluated as these factors are related to inflammation and to CRC risk . None ofE-DII: 1.05, 95%CI: 1.01–1.09; ORQ4vsQ1: 1.25, 95%CI: 1.02–1.55; p-value for trend: 0.06) but not in the fully adjusted model, where confidence limits overlapped unity . Sensitivity analyses did not alter the final model results and even when we excluded those BC cases that had more than six months between the data of diagnosis and the interview, results were not statistically significant ; however, there was no evidence of a linear dose response trend   also were investigated. No associations were found between E-DII  and HR+ and TN subtypes. A statistically significant increased HER2+ risk was observed when E-DII was evaluated as a categorical variable (ORe: 0.24) .Q4vsQ1: 1.48, 95%CI: 1.04–2.10, p-value for trend: >0.13) and postmenopausal women , no consistent evidence for effect-measure modification was observed by menopausal status, HRT and OC use, BMI, physical activity, and tobacco smoking   .p-value < 0.001). The ORs and 95% CI for the association between the inflammatory and antioxidant profile (combining information on E-DII and dietary NEAC) and CRC and BC risk are presented in Q4vsQ1: 1.48, 95%CI: 1.26–1.74; p-value for trend: <0.001). The correlation coefficient between E-DII and NEAC in the MCC-Spain study was −0.32 . Similar results for both outcomes were obtained when the combined categorical variable was used as the main exposure variable , as well as for participants classified at the fourth quartile (399 cases and 388 controls) compared to the first quartile (563 cases and 673 controls) . Those women who had higher values of meat consumption (above the median: ≥67 grams/day), had a 9% increased BC risk for each increment in the E-DII+NEAC score profile. High meat consumers classified at the highest category of the score (including 270 cases and 207 controls) had higher risk of developing BC . No evidence for effect-measure modification was observed in CRC models by low vs. high fiber, vegetables, and meat consumers  was clearly associated with increased CRC risk, and this risk was persistently shared across different tumor locations and all subgroups that were evaluated. Results regarding risk of BC do not support the hypothesis of an association; although non-statistically significant increased risks were observed when E-DII was analyzed both as a continuous variable and by quartiles. This study, including both outcomes, observed no changes when sensitivity analyses were carried out and did not observe evidence for effect-measure modification when subgroups were analyzed. The epidemiologic evidence on the association between DII and CRC risk has been reported in 4 prospective ,43,44,4512, and iron [Q4vsQ1: 1.50, 95%CI: 1.20–1.87), whereas a high adherence to the Mediterranean diet was inversely associated . Likewise, similar relative risks were observed for both patterns among males and females [Several dietary components and dietary patterns have been associated with CRC risk . A totaland iron . Most ofand iron . In agre females . The development and progression of BC has been linked, among other parameters, to inflammation . The eviper se is not taken into account in the construction of the DII, meat-derived macronutrients and micronutrients are included as pro-inflammatory factors. Thus, higher consumption of meat results in higher E-DII values . In addition, this category included more BC cases than controls (270 vs. 207). Meat intake has also been associated with increased BC risk in the MCC-Spain study, especially among postmenopausal women [Our study adds value to the scientific evidence because, up to now, none of the epidemiological studies cited above have evaluated the association between E-DII and cancer risk taking into account the total anti-oxidant dietary content. Dietary NEAC intake has been associated with a decreased risk of CRC in two large case-control studies, including the MCC-study ,35; wheral women . These fThis study is based on a relatively large sample size that enabled us to investigate specific colorectal and breast tumor subtypes, as well as different subgroups . Additionally, we have used a valid tool to assess the inflammatory potential of the diet, which has been investigated in many different studies, regions and different outcomes .Notwithstanding its strengths, there are some weaknesses in our study that should be mentioned. Case-control designs are prone to selection and recall biases. The MCC-study was designed with the goal of minimizing selection biases by recruiting population-based controls and cases . RegardiIn conclusion, the present study adds further evidence on the association between CRC risk and the inflammatory potential of the diet, as well as the combined effect of the inflammatory potential of the diet and its total dietary antioxidant capacity. The increased risk between these dietary scores and BC were not statistically significant. Additional studies with larger sample size should be performed to elucidate any possible association.


Oxidative stress has a role in the initiation and progression of breastcancer .OxidatiBy damaging DNA, activating oncogenes, and producing genomic instability,reactive oxygen species can initiate carcinogenesis . ReactivBreast cancer tumors with enhanced proliferative capacity produce high levelsof reactive oxygen species during their chronic cycles of ischemia, reperfusion, andangiogenesis, resulting in growth signaling . Cancer Many chemotherapeutic drugs and radiation treatments eliminate tumors throughthe induction of reactive oxygen species in cancer cells . HoweverEndogenous antioxidants, including non-enzyme and enzyme antioxidant defensesystems  act to prevent or limit tissue damage by scavenging free radicals incells . SuperoxExogenous antioxidants from dietary intake such as vitamin A, vitamin E, andVitamin C, may also have a role in the alleviation of oxidative stress, which couldreduce risk of breast cancer or slow progression of the disease . HoweverResults from observational epidemiologic studies conducted over the pastseveral decades demonstrate that physical activity and weight control are importantdeterminants of breast cancer risk and progression . OxidatiIn order to inform future studies of antioxidant chemo-preventive agents thatmay reduce risk of breast cancer initiation or progression, the complexities of howredox imbalance contributes to cancer and other chronic diseases should beconsidered. This includes the observation that although high levels of reactiveoxygen species disrupt cellular processes, lower levels of reactive oxygen speciescan act as cell signaling messengers by reversibly oxidizing protein thiol groupsand modifying protein structure . Thus, t


Similar all-cancer risk increases are observed for those above sex-specific WC guidelines. Mutual adjustment for WC attenuates the association between BMI and all-cancer risk, especially among females. Central adiposity appears to be a stronger predictor of all-cancer risk than body size.The importance of body size versus weight distribution for cancer risk is unclear. We investigated associations between measures of body size and shape and the risk of developing cancer. The study population consisted of 26,607 participants from the Alberta’s Tomorrow Project cohort. Two main measures of body shape and size were examined: i) body mass index (BMI) and ii) waist circumference (WC). Incident cancers were identified via linkage to the Alberta Cancer Registry. Cox proportional hazards models were used. Males and females classified as obese (BMI ≥ 30 kg /m Obesity is linked to increased cancer risk but the impact of body size versus weight distribution in determining the increased risk is unclear. Here the authors examined body mass index, waist circumference, and waist to hip ratio in relation to all-cancer incidence and incidence of seven individual cancers in a population of approximately 26,000 individual and conclude that central adiposity appears to be a stronger predictor of all-cancer risk than body size. The prevalence of obesity in Canada began to rise at a steady rate in the 1980s and recent projections suggest that by 2019, overweight and obese adults will outnumber normal-weight adults in half of the Canadian provinces2.In 2014 national surveys, 61.8% of Canadian men and 46.2% of Canadian women were classified as overweight  and American Institute for Cancer Research (AICR) conclude that excess body fatness increases the risk of 12 site-specific cancers including: esophageal4, pancreatic5, colorectal6, breast 7, endometrial8, kidney9, gallbladder10, stomach11, liver12, ovarian13, advanced prostate cancers14, and mouth, pharynx and larynx cancers15.Excess body weight is a strong risk factor for several chronic diseases including Type II diabetes, cardiovascular disease, and certain types of cancer2)16. In 2012, we estimated in Alberta, Canada the proportion of cancer cases attributable to excess body weight and found positive associations of overweight/obesity with risk of seven cancer sites ; overall, we concluded that 17% and 12% cancers among males and females, respectively, could be attributed to excess body weight17.The WCRF estimates that ~21% of these 11 types of cancer could be prevented each year in the United States if the entire adult population maintained a healthy body weight  examining anthropometric factors and endometrial cancer risk, as part of the WCRF Continuous Update Project, noted that a limited number of studies reported on waist circumference (WC) (4 out of 30) and waist-to-hip ratio (WHR) (5 out of 30), and few studies performed mutual adjustments for BMI and WC to elucidate their independent role in endometrial cancer riskTo our knowledge, few Canadian prospective cohort studies have examined the association between body size and shape and cancer risk. In the present study, we utilized data from Alberta’s Tomorrow Project (ATP), a prospective cohort study that included three measures of body size and shape available for participants enrolled at baseline: (i) BMI, (ii) WC, and (iii) WHR. Analyses using waist-to-height ratio (WtHR) were also conducted. Our two main objectives were: (i) to examine the associations between various measures of body shape and size and the risk of all-cancer and site-specific cancers, and; (ii) to examine which is the stronger predictor of cancer risk—total body mass or central body fatness.The analytic study population consisted of 26,607 participants who completed the HLQ, CDHQ, and PYTPAQ, consented to data linkage, and had no previous cancer diagnosis in the ACR. This sample size resulted in 328,095 person-years of follow-up. Of these, a total of 2370 participants  had an incident, primary cancer identified by the ACR during the follow-up period (mean of 7.2 years for cancer cases and 12.8 years for non-cancer cases).Table 2) were excluded, 26,360 participants  were included in these analyses. For males, positive trends with increasing BMI were observed for the incidence of all-cancer , colon cancer , non-Hodgkin lymphoma , and hematological cancers . Compared to males of normal weight, males with a BMI ≥ 30 kg/m2 had a 33% increased risk of all-cancer during the follow-up period . Males with a BMI ≥ 30 kg/m2 had 2.71  and 2.47  times the risk of colon cancer and non-Hodgkin lymphoma, respectively. No significant associations between BMI and the risk of prostate cancer, lung cancer, or leukemia were observed among males. For females, positive trends with increasing BMI were observed for all-cancers  and endometrial cancer . A significant inverse association between BMI and lung cancer was observed among females ; although this inverse trend was not significant among males, it was observed to be in the same direction. Higher BMI was also associated with a reduced risk of breast cancer among premenopausal women . Compared to females of normal weight, females with a BMI ≥ 30 kg/m2 had a 22% increased risk of all-cancer during the follow-up period . A strong positive association between a BMI ≥ 30 kg/m2 and the risk of endometrial cancer was observed . Overweight (BMI ≥ 25 kg/m2), but not obese, females had a significantly higher risk of colon cancer . No significant associations between BMI and the incidence of postmenopausal breast cancer, colon cancer, leukemia, non-Hodgkin lymphoma, and hematological cancers were observed among females. The aforementioned sex-specific findings for BMI and cancer risk were robust to sensitivity analyses whereby cancers occurring < 2 years following baseline data collection were removed with one exception; the significant inverse trend between higher BMI and the risk of premenopausal breast cancer observed in the multivariate-adjusted model  was not significant in the latency multivariate-adjusted model    and a reduced risk of lung cancer , in models that adjusted for all covariates as well as WC. For females, a BMI ≥ 30 kg/m2 was associated with an increased risk of endometrial cancer ; however, this risk was attenuated compared to results where WC was not adjusted for. Further risk reduction was also observed between increasing BMI and premenopausal breast cancer when adjusting for all covariates and WC .Results were no longer statistically significant for BMI and all-cancer risk when adjusting for all covariates, as well as WC as a dichotomous variable  who met other inclusion criteria. As compared with males who had a WC below guidelines, males with a WC above guidelines (≥102 cm) had a significantly elevated risk of all-cancer , as well as of three other site-specific cancers , which ranged from 1.56  for hematological cancers to 2.11  for non-Hodgkin lymphoma. Females with a WC above guidelines (≥88 cm) had a significantly elevated risk of all-cancer compared to females with a WC below guidelines , as well as endometrial cancer , but no other site-specific associations were observed. Sex-specific findings for WC and cancer risk were not markedly different when cancers occurring <2 years after baseline data collection were removed  in the multivariate-adjusted model. This risk was slightly attenuated in a final model additionally adjusted for BMI . Females in the highest and second highest WC quartiles were at an increased risk for endometrial cancer .We further examined WC in quartiles  who met other inclusion criteria. For males, significant positive trends with increasing quartiles of WHR were observed for the incidence of all-cancer , prostate cancer , and a marginally statistically significant positive trend was observed for colon cancer . Males in the highest quartile versus the lowest quartile of WHR had a 28%  increased risk of all-cancer and 42%  increased risk of prostate cancer during the follow-up period. Males in the second quartile versus the lowest quartiles of WHR had a significantly higher risk of non-Hodgkin lymphoma . For females, statistically significant positive trends with increasing quartiles of WHR were observed for the incidence of all-cancer  and endometrial cancer . Females in the highest quartile versus the lowest quartile of WHR had a 19%  increased risk of all-cancer during the follow-up period. An increased risk of endometrial cancer was observed for females in both the highest and second highest quartiles of WHR . When we examined sex-specific findings for quartiles of WHR and cancer risk in sensitivity analyses excluding cancers that occurred < 2 years after baseline data collection, effects estimates were not meaningfully altered  and colon cancer  though none of the individual HR estimates for quartiles reached statistical significance. For females, significant positive trends were observed between increasing WtHR and the incidence of all-cancer  and endometrial cancer ; women in the highest versus lowest quartile of WtHR had a significantly increased risk of developing all-cancer  and endometrial cancer  during the follow-up period. In a final model additionally adjusted for BMI, a significantly increased risk of developing endometrial cancer was no longer observed .Analyses on sex-specific results of WtHR quartiles for all- and site-specific cancer incidence are presented in Supplementary Table All-cancers and lung cancer for males and females combined were further examined by smoking status for BMI, WC, and WHR and these results are presented in Supplementary Tables 2) had a meaningfully increased risk of all-cancer, respectively, compared to their normal weight counterparts. Similar increases in risk of all-cancer were observed for those participants with WC above sex-specific guidelines. While our results indicate that both high BMI and high WC increase the risk of all-cancer, mutual adjustment for WC attenuated the association between BMI and all-cancer risk, especially among females. This finding suggests that central adiposity, as measured by WC, is a stronger predictor of all-cancer risk than BMI. Having a WHR in the highest versus the lowest quartile also increased the risk of all-cancers among both males and females.In this prospective cohort of over 25,000 participants, males and females classified as obese . While no statistically significant associations between the three measures of body shape and size and the risk of postmenopausal breast cancer were detected, the direction of risk was reversed by menopausal status; postmenopausal women with a BMI ≥ 30 kg/m22. Elevated concentrations of insulin are believed to activate the insulin-like growth factor pathway which, in turn, stimulates the growth and proliferation of cells and inhibits apoptosis, subsequently leading to tumor development23. Sex hormones, particularly estrogens, have been implicated in the relationship between body size and shape, and some site-specific cancers. For instance, following menopause estrogens are mainly produced in adipose tissue and rising estrogens in the blood are believed to encourage the development of breast and endometrial cancers via multiple pathways22. Mechanisms linking body size and shape to cancer pathogenesis continue to emerge and evolve including the concepts of obesity-induced hypoxia, shared genetic susceptibility, and the migration of adipose stromal cells24.The most common universal hypothesis explaining the molecular mechanism of obesity that promotes cancer is insulin resistance27. Previous studies report that increasing BMI is protective factor against the incidence of lung cancer in both males and females29 and we observed a statistically significant inverse trend between increasing BMI and lung cancer for females, which was in the same direction for males. This inverse association appears to be strongest among current smokers; however, we did not present such stratified analyses by sex because of the low statistical power. Existing literature indicates a mild positive association between increasing BMI and postmenopausal breast cancer27 and while our findings were not statistically significant, they were in the positive direction for women with a BMI ≥ 30 kg/m2. Consistent with our findings, strong positive associations between increasing BMI and endometrial cancer risk have been reported in numerous studies30.Our findings related to BMI and overall cancer risk within the ATP cohort are generally in agreement with the existing literature base. Systematic reviews and meta-analyses have also detected a stronger association between increasing BMI and the risk of colon cancer among males than females, although these sex differences are not fully understood31, it is valuable to consider additional indicators of body shape and size including WC and WHR. Interestingly, we found differences in the direction of risk for premenopausal breast when considering BMI versus WHR; higher BMI was associated with a reduced risk of breast cancer whereas those in the second and third quartiles of WHR had an increased risk of breast cancer and those in the fourth quartile of WHR had a decreased risk of breast cancer, though these estimates were not statistically significant. While some studies have associated increased central obesity and WHR with adverse risk among premenopausal women34, this association has not been consistently observed35, and a meta-analysis of central obesity found no overall effect in premenopausal women36. Mutations in the breast cancer genes (BRCA) 1 and 2 increase the risk of developing young-onset breast cancer;38 however, only a small number of breast cancers diagnosed among young women (~5–10%) are attributable to these mutations38, suggesting that other unknown genetic, lifestyle, or environmental factors are involved.Given that BMI has been criticized for not providing any indication of the fat distribution39 and thus, are likely not representative of the entire Alberta population. For example, the majority of our sample was female (62%) and Caucasian (91%). Second, the ATP cohort has a higher proportion of participants who have a BMI ≥ 25 kg/m2 compared to participants in the same age range (35–69 years) from Alberta who participated in Cycle 1.1 of the Canadian Community Health Survey (CCHS) collected in 2005, available from Statistics Canada; 75.3% of males and 59.2% of females from the present analyses were classified as overweight or obese versus 65.4% of males and 48.1% of females in the CCHS subsample40. Accordingly, the generalizability of our findings is limited. Third, although we included cancer sites with ≥100 incident cases, there were small cell counts in some cases after body shape and size variables were divided into relevant categories. This sample size resulted in some less precise site-specific estimates that should be interpreted with caution (e.g. leukemia and Non-Hodgkin lymphoma). Fourth, the use of self-reported measures of height, weight and waist and hip circumferences that were used to derive our exposure variables may have been influenced by a social desirability bias, which may have introduced non-differential misclassification bias. Studies to validate these particular measures should be considered going forward. Furthermore, future studies examining the relationship between body shape and size, and cancer risk should consider using more objective measures of adiposity such as dual-energy X-ray absorptiometry, which are not prone to information biases. Finally, we assumed that our exposure variables remained constant during the follow-up period; however, given that several studies have found that BMI tends to increase into early old age43, our estimates are likely underestimations of the true HRs.Several limitations to the data presented must be acknowledged. First, participants who were enrolled in our sample were not diverse in terms of some sociodemographic variablesStrengths of this paper include: (i) the detailed instructions in the HLQ and measuring tape provided to participants in order to collect their measurements, which likely improved the accuracy of self-reporting, (ii) the availability of data on all-cancer as well as eight site-specific cancers in the same cohort, and (iii) the sensitivity analyses performed that excluded cancers occurring <2 years after baseline data collection were removed to decrease the possibility of reverse causation, and (iv) the ability to establish temporality through the use of a large prospective cohort study.These analyses suggest that central adiposity is a strong predictor of all-cancer risk, especially among females and highlight the importance of weight distribution. Public health interventions that promote weight management via multifaceted approaches can contribute to cancer primary prevention. Further, secondary cancer prevention measures may be improved by identifying targeting screening programs to individuals who are at higher risk for developing cancer due to the distribution of their body weight. We recommend that future research studies investigate changes in body shape and size over time related to cancer risk and use more accurate measures of body size and weight distribution.39. In brief, participants were recruited through eight waves of telephone-based random digit dialing using regional health authority boundaries within the province of Alberta. Participants were eligible if they were between the ages of 35 and 69 years, had no previous history of cancer (other than non-melanoma skin cancer), planned to reside in Alberta for at least one year, were not pregnant at the time of enrollment, and were able to complete questionnaires in English.We analyzed data from ATP, a prospective cohort study of adults in the province of Alberta, Canada. The ATP, which started in 2000, aims to investigate factors that influence cancer and chronic disease risk. A full description of the study design and recruitment for ATP has been previously publishedn = 31,121) enrolled in ATP and completed the Health and Lifestyle Questionnaire (HLQ). The HLQ included domains related to socio-demographics, personal and family health history, lifestyle, and anthropometric measurements. Twelve weeks after completing the HLQ, participants were mailed the Canadian Diet History Questionnaire (CDHQ) and the Past Year Total Physical Activity Questionnaire (PYTPAQ). The present study focused on a subset of the cohort who completed the HLQ, CDHQ, and PYTPAQ and consented to data linkage with administrative databases, including the provincial cancer registry  and were instructed to use a scale, set to zero, to measure their current weight. All measurements were to be made in a single session at least 2 h after eating a meal, and preferably with the help of another adult. For height, participants were instructed to remove their shoes, stand straight with their back and heels against the wall and record their height in feet and inches. For weight, participants were instructed to remove their shoes, wear light clothing and record their weight in pounds. Instructions for abdominal circumference stated, “Measure one inch above your navel or belly button, even if this is not your usual waistline”. For buttocks circumference, participants were instructed to “Slide the tape measure up and down until you find the largest spot between your waist and thighs”. Diagrams were included to aid in identifying the correct measurement location for the abdomen and buttocks. Participants were instructed to take at least two measurements for each anthropometric measure. Prior to deriving single variables for height, weight, BMI, abdominal and buttocks circumference, research staff at ATP applied range checks to body measurements and contacted participants in the case of missing, contradictory, or extreme measurements.2) as a continuous variable and as a categorical variable classified as normal weight (BMI 18.5 to < 25 kg/m2), overweight (BMI 25 to <30 kg/m2), and obese (BMI ≥ 30 kg/m2). Given the U-shaped relationship between BMI and all-cause mortality that has been observed in several populations45, we decided to exclude individuals (n = 181) classified as underweight (BMI < 18.5 kg/m2) rather than combining them with those classified as normal weight. WC was also considered as a continuous and categorical variable using the World Health Organization (WHO) cut-off points for a substantially increased risk of metabolic complications: ≥102 cm for males and ≥88 cm for females46. We planned to examine categories of WHR based on the WHO cut-offs points for a substantially increased risk of metabolic complications 46; however, since 84% of males in our sample had a WHR above the recommended WHO guideline, we examined quartiles of WHR instead.We considered BMI  to identify incident, primary cancers (with the exception of non-melanoma skin cancers) up to June 2017. The coding of new cancer cases by site is based on the International Classification of Diseases for Oncology, Third EditionWe considered all incident, primary cancer cases as well as eight site-specific cancers where ≥100 incident cases occurred in the cohort: breast, colon (includes cancers of the colon and recto-sigmoid junction), prostate, lung, endometrial, non-Hodgkin lymphoma, leukemia, and hematological cancers .We adjusted for multiple covariates based on known associations with both body shape and size, and cancer. All adjusted models included the following variables: age, sex, self-reported ethnicity (white/other), marital status , highest education level (high school or less/some post-high school education/post-high school certificate or degree), total household income , geographical area of residence , smoking status (never/former/current), mean energy intake , mean alcohol intake (grams of ethanol per day), total weekly metabolic output from all activities , ever being diagnosed with diabetes by a physician (yes/no), and family history of cancer (yes/no). Other covariates adjusted for in site-specific models include: menopausal status (pre-menopause/post-menopause) and oral contraceptive use (0–5 years/>5 years) for breast and endometrial cancers; pack-years of cigarette smoking for lung cancer; and mean fiber intake (grams per day) for colon cancer. We included a dichotomous (yes/no) variable for indicating ever having cancer screening tests in adjusted models for breast cancer , colon cancer (blood stool test and/or sigmoidoscopy or colonoscopy), and prostate cancer . Lastly, for breast cancer, we included a dichotomous (yes/no) variable for ever having used female hormones  for menopause.P values of the Wald Z-tests. For all analyses, a two-tailed P-value of <0.05 was considered statistically significant and Stata software (version 14.2) was used49.Participant follow-up time was calculated from entry into the study (based on exact age at the time of HLQ completion) to date of cancer diagnosis for cases (based on exact age at diagnosis), or to the end of follow-up for non-cancer cases (based on their exact age at the time of data linkage with ACR in June 2017). Descriptive statistics were used to characterize the cohort by sex and cancer incidence status. We used Cox proportional hazards models to compute age-adjusted and multivariate-adjusted hazards ratios (HR) and 95% confidence intervals (CIs) of all-cancer and site-specific cancer incidence for the three measures of body shape and size  for males and females, separately. Unless otherwise noted, the HRs we reported are from the multivariate-adjusted models. We conducted sensitivity analyses, where incident cancers occurring less than two years after baseline data collection were excluded, in order to minimize the potential of reverse causation . Tests of linear trend were performed by scoring the categories of the various measures of body shape and size and imputing the score as a continuous term in the Cox proportional hazard model and reporting the associated Supplementary Tables