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Methylation-based markers of aging and lifestyle-related factors and risk of breast cancer: a pooled analysis of four prospective studies

Breast Cancer Research volume 24, Article number: 59 (2022) Cite this article

Abstract

Background

DNA methylation in blood may reflect adverse exposures accumulated over the lifetime and could therefore provide potential improvements in the prediction of cancer risk. A substantial body of research has shown associations between epigenetic aging and risk of disease, including cancer. Here we aimed to study epigenetic measures of aging and lifestyle-related factors in association with risk of breast cancer.

Methods

Using data from four prospective case–control studies nested in three cohorts of European ancestry participants, including a total of 1,655 breast cancer cases, we calculated three methylation-based measures of lifestyle factors (body mass index [BMI], tobacco smoking and alcohol consumption) and seven measures of epigenetic aging (Horvath-based, Hannum-based, PhenoAge and GrimAge). All measures were regression-adjusted for their respective risk factors and expressed per standard deviation (SD). Odds ratios (OR) and 95% confidence intervals (CI) were calculated using conditional or unconditional logistic regression and pooled using fixed-effects meta-analysis. Subgroup analyses were conducted by age at blood draw, time from blood sample to diagnosis, oestrogen receptor-positivity status and tumour stage.

Results

None of the measures of epigenetic aging were associated with risk of breast cancer in the pooled analysis: Horvath ‘age acceleration’ (AA): OR per SD = 1.02, 95%CI: 0.95–1.10; AA-Hannum: OR = 1.03, 95%CI:0.95–1.12; PhenoAge: OR = 1.01, 95%CI: 0.94–1.09 and GrimAge: OR = 1.03, 95%CI: 0.94–1.12, in models adjusting for white blood cell proportions, body mass index, smoking and alcohol consumption. The BMI-adjusted predictor of BMI was associated with breast cancer risk, OR per SD = 1.09, 95%CI: 1.01–1.17. The results for the alcohol and smoking methylation-based predictors were consistent with a null association. Risk did not appear to substantially vary by age at blood draw, time to diagnosis or tumour characteristics.

Conclusion

We found no evidence that methylation-based measures of aging, smoking or alcohol consumption were associated with risk of breast cancer. A methylation-based marker of BMI was associated with risk and may provide insights into the underlying associations between BMI and breast cancer.

Introduction

Numerous studies have investigated the association of blood DNA methylation and breast cancer risk, for example, at breast cancer-specific genes [1,2,3], and overall found mixed results [4]. Lower global levels of DNA methylation are thought to reflect genomic instability and have been hypothesised to increase the risk of cancer [5], but while several studies were conducted in the context of breast cancer [6,7,8,9] they together suggested that there is no substantial association [10]. At individual cytosine-guanine (CpG) sites, our meta-analysis of individual-participant data (1,663 incident cases and matched controls) from the Melbourne Collaborative Cohort Study (MCCS), the European Prospective Investigation into Cancer and Nutrition (EPIC) (EPIC-Italy and EPIC-IARC), and the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial (PLCO) did not find evidence of associations with breast cancer risk [10]. In contrast, a case-cohort analysis within the Sister Study (1,566 breast cancer cases), a US cohort of women with a sister diagnosed with breast cancer, revealed associations at over 2,000 CpGs [11]. Another study with a large sample size found that genetically predicted methylation levels were associated with breast cancer risk [12], but it is unclear how predicted methylation relates to measured methylation, given that methylation varies with age and exposures accumulated over the life course [13,14,15,16].

Methylation-based markers of aging, such as Horvath-based [17], Hannum-based [18], PhenoAge [19] and GrimAge [20], have become popular tools to evaluate the association between biological aging and risk of disease. While the ‘first-generation’ measures (Horvath-based and Hannum-based) were developed to predict age accurately, PhenoAge and GrimAge are methylation-based predictors of composite measures (using clinical and physiological data) that are predictive of mortality. The residual of each of these measures on chronological age, named ‘age acceleration’, best reflects the concept of biological aging. A positive association between epigenetic aging (Horvath first-generation measure) and risk of breast cancer was first reported in an EPIC-IARC study [8], and later confirmed in the Sister Study for Horvath, Hannum and PhenoAge measures [21], but not for GrimAge [22]. Only the age acceleration based on Horvath methylation age [17] was therefore studied in relation to breast cancer risk in both previous published studies, so there is a need to accumulate evidence, particularly in women unselected for family history. Associations of epigenetic aging measures with risk of several other types of cancer were also observed in the MCCS, and these tended to be stronger for PhenoAge and GrimAge than for the first-generation measures [23, 24].

Factors other than age, mainly tobacco smoking [14, 25], alcohol consumption [15, 26] and body mass index [13, 27, 28] strongly influence blood DNA methylation and may also increase the risk of breast cancer. Similar to epigenetic aging, methylation marks of lifestyle could be useful markers to increase the precision with which we measure their association with cancer risk. These could reflect unmeasured past and cumulative exposures, imperfect assessments provided by questionnaires, or different individual responses to exposure; epigenetic predictors of lifestyle may therefore have potential to improve the prediction of breast cancer risk.

The aim of this study was to examine the association of previously derived 1) seven methylation-based measures of aging, and 2) methylation-based measures of body mass index, alcohol consumption and tobacco smoking, with breast cancer risk in a meta-analysis of individual-participant data including 1,655 breast cancer cases sampled from the MCCS, EPIC and PLCO.

Methods

Data sources

We used data from four methylation studies nested within three prospective cohorts of European ancestry participants: the Melbourne Collaborative Cohort Study (MCCS) [29], the European Prospective Investigation into Cancer and Nutrition (EPIC) (EPIC-Italy [7] and EPIC-IARC [8]), and the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial (PLCO) [30]. Details about these cohorts and design of the methylation studies were described previously [10] and are provided in the Additional file 1. We used the same case selection as in our previous meta-analysis. Ductal carcinoma in situ cases were excluded from the analysis [10].

DNA extraction, bisulphite conversion and DNA methylation data processing

Methods relating to DNA extraction and bisulphite conversion, and DNA methylation data processing have been described previously and are detailed in Additional file 1 and are the same as in our previous pooled analysis [10]. In brief, the MCCS, EPIC-Italy and EPIC-IARC measured DNA methylation using the Illumina Infinium 450 k BeadChip methylation array, and PLCO used the llumina InfiniumEPIC 850 k BeadChip methylation array. The pipeline for normalization of the methylation data was the same across the four studies (Additional file 1). β-values were calculated for each CpG site for each sample using the R package minfi. β-values were calculated for each CpG site for each sample using the R package minfi. Methylation measures with a detection P-value higher than 0.01 were considered missing. Samples with > 5% of CpG methylation measures missing were excluded, and CpGs with values missing for more than 20% of samples were excluded. White blood cell proportions were estimated using the Houseman algorithm modified by Jaffe and Irizarry [31, 32], using the R function estimateCellCounts implemented in minfi, or Horvath’s calculator, to derive the proportion of CD8 + T cells, CD4 + T cells, NK cells, B cells, monocytes and granulocytes.

Methylation-based measures

Epigenetic aging

We used the normalised DNA methylation data to calculate the epigenetic measures of aging developed by Horvath [7] and Hannum et al. [8], as well as PhenoAge [9], and GrimAge [20] (composite biomarkers enriched for adverse phenotypes) as these have been shown to be accurate predictors of chronological age, and their deviation from chronological age (i.e. ‘age acceleration’ [AA]) was consistently found to be associated with risk of disease, cancer and death. These measures are calculated using methylation data at 353, 71, 513 and 1,030 CpGs, respectively, and were obtained using Horvath’s online calculator https://dnamage.genetics.ucla.edu/new [17, 19, 20]. Their respective age acceleration measures, defined as the residuals of the regression on chronological age, were also computed using the online calculator. Similar to other publications [21, 23], AA-Horvath and AA-Hannum measures were modified based on cell proportions. Specifically, ‘intrinsic’ epigenetic age acceleration (IEAA) is a measure of age acceleration independent of age-related changes in blood cell composition. It is computed as the residuals of the methylation age (Horvath or Hannum) on chronological age and methylation-based blood cell count estimates. ‘Extrinsic’ epigenetic age acceleration (EEAA) is computed as the residual of the Horvath methylation age on chronological age and a weighted average of age-related changes in blood cell composition. It is thought to be a measure of immune system aging. Both IEAA measures (IEAA-Horvath and IEAA-Hannum) and EEAA were estimated via the online calculator.

Methylation-based predictors of lifestyle

We considered a priori three established lifestyle factors associated with breast cancer risk for which there is substantial evidence of an association with DNA methylation in blood, i.e. smoking [14, 25], alcohol consumption [15, 26] and BMI [13, 27, 28]. We used the predictors by McCartney et al. [33] as these were developed and validated in a large sample of participants of mainly European ancestry (Generation Scotland) using regularised regression. The proportion of trait variance explained by these predictors was previously reported to be 61%, 12.5% and 12.5% for log of smoking pack-years, alcohol intake and BMI, respectively. Methylation predictors for BMI, smoking and alcohol consumption were calculated as the weighted average of methylation β-values at the corresponding number of CpGs, using weights available from the original publication at 1,109, 233, and 450 CpGs, respectively [33]. In each study, the methylation scores for each participant were calculated after exclusion of CpGs with missing methylation values. Each predictor was regressed on its respective risk factor—log(BMI), log(smoking pack-years) and log(alcohol consumption)—to obtain adjusted measures.

Statistical analysis

Linear regressions between each trait and its respective epigenetic predictors were conducted to assess their association and variance explained in each predictor (Additional file 1: Table S1).

The four studies individually performed conditional (MCCS, EPIC-Italy, EPIC-IARC) or unconditional (PLCO) logistic regression to estimate the odds ratio (OR) and 95% confidence interval (95%CI) for breast cancer risk per one standard deviation (SD) increase for each of the age acceleration, smoking, alcohol intake and BMI methylation-based measures. Associations were also calculated per five-year AA increase for comparison with other studies.

Models adjusted were appropriate for the matching variables specific to each study (see Additional file 1), cell-type proportions estimated with the Houseman algorithm (percentage CD8T + , CD4T + , NK, B-cell, monocytes, granulocytes) and other variables to account for batch effects, such as plate or surrogate variable analysis (SVA) and additional adjustment for smoking (continuous pack-years), alcohol intake (continuous, grams/day) and BMI (continuous, kg/m2). Models with i) no adjustment and ii) adjustment for white blood cell proportions only, yielded very similar results and are shown in Additional file 1. Participants with missing data in any of the adjusting variables were excluded from the analysis.

Subgroup analyses within each study were carried out by conducting the same analyses (Model 1) for the following case characteristics: age at blood draw (< 50; ≥ 50 years old), time between blood draw and diagnosis (< 5; ≥ 5 years), oestrogen receptor (ER) positivity status, stage (I; II or higher).

For all analyses, estimates of pooled OR and 95%CI were calculated using fixed-effects meta-analysis, and P-values were calculated using the Wald test statistic. Heterogeneity in the ORs across studies was examined using the I2 statistic.

Results

A total of 1,655 breast cancer cases were included in the analysis. The median age at blood draw was 53 years in EPIC-IARC and EPIC-Italy, 57 years in the MCCS and 62 years in PLCO. The median time from blood draw to diagnosis ranged between 6.5 years (EPIC-Italy) and 8.4 years (PLCO). Most tumours were ER positive (71% in the PLCO to 83% in EPIC samples) and diagnosed at low stage (~ 60%). For all studies, there were no large case–control differences in terms of smoking, alcohol consumption and BMI (Table 1). The description of the methylation-based predictors for each study is shown in Table 2. The range of variance explained of age by epigenetic aging measures across cohorts was: Horvath: 39% to 60%; Hannum: 48% to 64%; PhenoAge: 32% to 50%; GrimAge: 50% to 69%. The variance explained by methylation-based predictors for BMI ranged from 14 to 22%; for smoking from 41 to 54% and for alcohol consumption from 3 to 9%. All measures were strongly associated with their respective risk factor (Additional file 1: Table S1). All adjusted measures had mean 0 and standard deviation 1 and were uncorrelated with their respective variable.

Table 1 Characteristics of study participants
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Table 2 Description of methylation-based measures
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Although the Horvath measures of epigenetic aging AA and IEAA were associated with risk in EPIC-IARC (similar to [8], AA-Horvath: OR per SD = 1.22, 95%CI: 1.02–1.45, IEAA-Horvath: OR = 1.22, 95%CI: 1.03–1.45), no evidence of association was found in the other cohorts (for AA: point ORs of 0.94, 0.89 and 1.03 in MCCS, EPIC-Italy, and PLCO, respectively). The pooled estimates were consistent with a null association: OR = 1.02, 95%CI: 0.95–1.10 for AA-Horvath and OR = 1.03, 95%CI: 0.96–1.11 for IEAA-Horvath after adjustment for white cell proportions, body mass index, smoking and alcohol consumption (Table 3). Similar findings were obtained for the EEAA, AA-Hannum and IEAA-Hannum measures, with pooled OR per SD = 1.02 (0.93–1.11), OR = 1.03 (0.95–1.12) and OR = 1.04 (0.96–1.12), respectively. AA-PhenoAge and AA-GrimAge age-adjusted measures also showed no association with breast cancer risk, with OR per SD: 1.01 (0.94–1.09) and OR = 1.03 (0.94–1.12), respectively), Table 3. The results for all epigenetic aging measures were virtually unchanged in models without adjustment for white blood cell proportions or lifestyle-related factors (Additional file 1: Table S2).

Table 3 Odds ratiosa for the association between methylation-based measures of breast cancer risk factors and risk of breast cancer
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Associations for methylation-based predictors of lifestyle-related factors are shown in Table 3. The predictor of BMI was positively associated with breast cancer risk in the pooled analysis with an OR of 1.10 per SD (95%CI: 1.02–1.18), with little heterogeneity across cohorts (I2 = 26%). This association was virtually not attenuated when considering the adjusted measure, i.e. BMI predictor regressed on BMI: OR = 1.09, 95%CI: 1.01–1.17 (I2 = 12%). Estimates were similar in unadjusted models (Additional file 1: Table S2). There was limited evidence that the methylation-based predictors of smoking (OR = 1.04, P > 0.05) and alcohol consumption (OR = 1.0, P > 0.05), or their respective adjusted measures, were associated with breast cancer risk (Table 3).

None of the associations of epigenetic aging or lifestyle measures with breast cancer risk showed substantial heterogeneity by age at blood draw, time to diagnosis, ER positivity or tumour stage at diagnosis (Table 4); for AA-GrimAge, the association appeared stronger for ER-negative cases (OR = 1.18, 95%CI: 1.00–1.40). The adjusted BMI measures appeared somewhat more strongly associated with risk for women diagnosed within five years from blood draw (OR = 1.15, 95%CI: 1.01–1.32) compared with those diagnosed more than five years after blood draw (OR = 1.08, 95%CI: 1.00–1.17).

Table 4 Odds ratiosa (pooled analysis) for subgroup analyses of the association between methylation-based measures of aging and lifestyle (regressed on their respective risk factors) and risk of breast cancer (N = 1,655)
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Discussion

We have assessed seven measures of epigenetic aging and three methylation-based predictors of lifestyle for their association with breast cancer risk in a large sample (1,655 cases) of women from Western countries (Australia, Europe and the USA). We found overall no associations between measures of epigenetic aging and risk of breast cancer. A positive association was observed for the BMI methylation score, but not for smoking and alcohol consumption.

To our knowledge few studies have investigated the association of epigenetic aging with breast cancer risk. We included in this meta-analysis the samples for which an association was reported previously in EPIC-IARC [8]. Slightly different models were used but the results were very similar. The association previously observed in EPIC-IARC was restricted to postmenopausal women (per 1-year IEAA-Horvath: OR = 1.06, 95%CI: 1.02–1.11) compared with OR = 1.00 for premenopausal women. We found no evidence of an association in our meta-analysis, including when restricted to ages older than 50 years. Our results are overall consistent with the findings from the Sister Study [34], which reported relatively weak associations: based on 1,566 cases, per 5-year AA-Hannum: hazard ratio (HR) = 1.10, 95%CI, 1.00–1.21, AA-Horvath: HR = 1.08, 95%CI = 1.00–1.17, and AA-PhenoAge: HR = 1.15, 95%CI = 1.07–1.23. In our study, the ORs expressed per 5-year AA were compatible for AA-Hannum and AA-Horvath (HR = 1.02, 95%CI, 0.94–1.10 and HR = 1.01, 95%CI = 0.94–1.08, respectively) and more discrepant for AA-PhenoAge: HR = 1.00, 95%CI = 0.95–1.06. Similar to our findings, the authors did not find substantial heterogeneity by, for example, menopausal or ER-positivity status. For AA-GrimAge, the authors expressed the association per standard deviation [22] and found HR = 1.06, 95%CI: 0.98, 1.14, which is also similar to our study OR = 1.03, 95%CI: 0.94–1.12. Although AA-GrimAge appeared somewhat more strongly associated with risk for postmenopausal women in the Sister Study, the evidence for heterogeneity was weak and there was no indication of this in our data (HR = 1.03 for women aged ≥ 50 years at blood draw).

The main differences between the cohorts included in our meta-analysis and the Sister Study are that it was enriched for family history of breast cancer and had substantially shorter length of follow-up than ours (for the cases, mean time to diagnosis of 3.9 years, compared with > 6 years for all studies we included). We nevertheless did not observe that OR estimates were larger when blood was collected closer to diagnosis (within 5 years: OR ~ 1.01, 0.98, 1.01, 1.04 for AA-Horvath, AA-Hannum, AA-PhenoAge and AA-GrimAge, respectively). The study of Durso and colleagues [35] compared Horvath and Hannum age acceleration measures between 233 Italian women who developed breast cancer (mean age at recruitment: 52.4 years, mean time to diagnosis: 3.8 years) and cancer-free controls and found no evidence of an association. A study of multiple health outcomes using Generation Scotland data included 83 incident breast cancer cases, diagnosed over 13 years of follow-up in women aged ~ 51 years at baseline [36]. A tendency for risk associations to be positive was observed: per SD, AA-Horvath, HR = 1.01 (P = 0.95), AA-Hannum: HR = 1.24 (P = 0.07), AA-PhenoAge: HR = 1.36 (P = 0.01), and AA-GrimAge = 1.19 (P = 0.16), respectively, in age-adjusted models. The literature to date therefore includes, to our knowledge, approximately 3,550 breast cancer cases and is consistent with a weak (of roughly 8% increase per 5-year AA for AA-PhenoAge) or null association between epigenetic aging measured in blood and breast cancer risk.

There has not been to our knowledge any study examining methylation-based predictors of lifestyle-related factors with risk of breast cancer. A handful of studies have examined risk of overall mortality [33], survival from oropharyngeal cancer [37] and risk of several types of cancer in the Melbourne Collaborative Cohort Study [38]. Another study used the Cancer Genome Atlas datasets to develop lifestyle predictors based on tumour DNA methylation [39] and found that the BMI-associated methylation signature was predictive of shorter breast cancer survival. For the methylation-based predictors used in our study, the variance explained was somewhat higher than that originally reported by McCartney et al. [33] for BMI (12%), but somewhat lower for smoking and alcohol consumption (61% and 12%, respectively). For smoking, it may be because it was trained to predict log (pack-years) in current smokers, and our analysis also included former smokers; analyses of the MCCS data showed that the R2 was 66% when former smokers were excluded (not shown). Other methylation-based measures of lifestyle have been developed showing similar accuracy, e.g. for alcohol [26], or smoking [25, 40], and were not tested in the current study; we chose to use these predictors because they were developed using a large sample size of people of similar ancestry (Scottish) and were well validated. In MCCS analyses of other cancer types, the choice of predictor did not appear to make a substantial difference in the observed associations [38]. In another analysis of the Sister Study data, the authors used as inputs to predict breast cancer risk 36 methylation-based measures of biological aging and physiological characteristics and methylation values at 100 individual CpGs (i.e. using altogether methylation values at thousands of CpGs) and derived a risk score that showed reasonable performance with an area under the curve of 0.63, which was similar to, and independent of, the association observed for the 313-SNP polygenic risk score [41]. We did not attempt to combine methylation scores in our study because most associations were weak, but it is likely that this type of approach may yield improvements to breast cancer risk prediction in the future.

That we observed only weak or null associations may be explained by the fact that none of BMI, alcohol consumption or smoking are strong risk factors for breast cancer. Previous studies have generally found weak to moderate associations [33, 37, 38], except for lung cancer in the MCCS [38], for which the effect of smoking is dramatic. We had hypothesised that methylation predictors of BMI, alcohol and smoking could contain more information about lifestyle than the measured risk factors—for example, exposures accumulated over the lifetime, in particular, during sensitive periods such as early life or the periconceptional period, which could be better captured by DNA methylation compared with questionnaires at older ages. BMI has consistently been found to be positively associated with risk of breast cancer for postmenopausal women and negatively for pre-menopausal women [42]; we did not observe this using methylation scores as the estimates of associations were similar by age at blood draw (< 50 years: HR = 1.10 [0.93–1.30] and ≥ 50 years: HR = 1.10 [1.02–1.19]). The association we observed for BMI might also reflect the combined effect of several aspects of obesity beyond BMI [43] that could be captured by changes in DNA methylation. For other breast cancer risk factors, there is to date no convincing evidence that they are strongly associated with blood DNA methylation changes, e.g. for mammographic density [44] or lifetime oestrogen exposure [45]. Additional risk factors for breast cancer were not adjusted for, but this would probably make little difference to the results given their confounding effect on the lifestyle methylation-breast cancer association is likely small.

The main strength of our study is the largest sample size to date of ~ 1,650 cases with long follow-up and comprehensive assessment of epigenetic measures for association with risk. The same analysis method was applied across cohort datasets and participants were representative of the general population. Limitations of our study include the relative heterogeneity of the pooled samples; even though most participants were of European ancestry, there was some variation in terms of age at inclusion, follow-up time or sample processing. All studies used the same pipeline for normalisation of the data, but PLCO used the EPIC assay, which may result in small measurement differences.

Conclusion

Our study found overall weak associations of methylation-based measures of aging and lifestyle-related with breast cancer risk. The association observed for a BMI methylation score might provide insights in the underlying association between BMI and breast cancer and should be further investigated in additional studies.

Availability of data and materials

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

References

  1. Wong EM, Southey MC, Fox SB, Brown MA, Dowty JG, Jenkins MA, Giles GG, Hopper JL, Dobrovic A. Constitutional methylation of the BRCA1 promoter is specifically associated with BRCA1 mutation-associated pathology in early-onset breast cancer. Cancer Prev Res (Phila). 2011;4(1):23–33.

    Article  CAS  Google Scholar 

  2. Flanagan JM, Munoz-Alegre M, Henderson S, Tang T, Sun P, Johnson N, Fletcher O, dos Santos SI, Peto J, Boshoff C. Gene-body hypermethylation of ATM in peripheral blood DNA of bilateral breast cancer patients. Hum Mol Genet. 2009;18(7):1332–42.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Brennan K, Garcia-Closas M, Orr N, Fletcher O, Jones M, Ashworth A, Swerdlow A, Thorne H, Riboli E, Vineis P. Intragenic ATM methylation in peripheral blood DNA as a biomarker of breast cancer risk. Can Res. 2012;72(9):2304–13.

    Article  CAS  Google Scholar 

  4. Tang Q, Cheng J, Cao X, Surowy H, Burwinkel B. Blood-based DNA methylation as biomarker for breast cancer: a systematic review. Clin Epigenet. 2016;8(1):1–18.

    Article  CAS  Google Scholar 

  5. Brennan K, Flanagan JM. Is there a link between genome-wide hypomethylation in blood and cancer risk? Cancer Prev Res (Phila). 2012;5(12):1345–57.

    Article  CAS  Google Scholar 

  6. Severi G, Southey MC, English DR, Jung C-H, Lonie A, McLean C, Tsimiklis H, Hopper JL, Giles GG, Baglietto L. Epigenome-wide methylation in DNA from peripheral blood as a marker of risk for breast cancer. Breast Cancer Res Treat. 2014;148(3):665–73.

    Article  CAS  PubMed  Google Scholar 

  7. van Veldhoven K, Polidoro S, Baglietto L, Severi G, Sacerdote C, Panico S, Mattiello A, Palli D, Masala G, Krogh V, et al. Epigenome-wide association study reveals decreased average methylation levels years before breast cancer diagnosis. Clin Epigenet. 2015;7(1):67.

    Article  CAS  Google Scholar 

  8. Ambatipudi S, Horvath S, Perrier F, Cuenin C, Hernandez-Vargas H, Le Calvez-Kelm F, Durand G, Byrnes G, Ferrari P, Bouaoun L, et al. DNA methylome analysis identifies accelerated epigenetic ageing associated with postmenopausal breast cancer susceptibility. Eur J Cancer. 2017;75:299–307.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Sturgeon SR, Pilsner JR, Arcaro KF, Ikuma K, Wu H, Kim SM, Chopra-Tandon N, Karpf AR, Ziegler RG, Schairer C, et al. White blood cell DNA methylation and risk of breast cancer in the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial (PLCO). Breast Cancer Res. 2017;19(1):94.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  10. Bodelon C, Ambatipudi S, Dugué P-A, Johansson A, Sampson JN, Hicks B, Karlins E, Hutchinson A, Cuenin C, Chajès V, et al. Blood DNA methylation and breast cancer risk: a meta-analysis of four prospective cohort studies. Breast Cancer Res. 2019;21(1):62.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  11. Xu Z, Sandler DP, Taylor JA. Blood DNA methylation and breast cancer: a prospective case-cohort analysis in the Sister Study. J Natl Cancer Inst. 2019;112:87–94.

    Article  PubMed Central  CAS  Google Scholar 

  12. Yang Y, Wu L, Shu X-O, Cai Q, Shu X, Li B, Guo X, Ye F, Michailidou K, Bolla MK. Genetically predicted levels of DNA methylation biomarkers and breast cancer risk: data from 228,951 women of European descent. J Natl Cancer Inst. 2019;112:295–304.

    Article  PubMed Central  CAS  Google Scholar 

  13. Geurts YM, Dugué PA, Joo JE, Makalic E, Jung CH, Guan W, Nguyen S, Grove ML, Wong EM, Hodge AM, et al. Novel associations between blood DNA methylation and body mass index in middle-aged and older adults. Int J Obes (Lond). 2018;42(4):887–96.

    Article  CAS  Google Scholar 

  14. Dugué PA, Jung CH, Joo JE, Wang X, Wong EM, Makalic E, Schmidt DF, Baglietto L, Severi G, Southey MC, et al. Smoking and blood DNA methylation: an epigenome-wide association study and assessment of reversibility. Epigenetics. 2020;15(4):358–68.

    Article  PubMed  Google Scholar 

  15. Dugué PA, Wilson R, Lehne B, Jayasekara H, Wang X, Jung CH, Joo JE, Makalic E, Schmidt DF, Baglietto L, et al. Alcohol consumption is associated with widespread changes in blood DNA methylation: Analysis of cross-sectional and longitudinal data. Addict Biol. 2021;26(1): e12855.

    Article  PubMed  CAS  Google Scholar 

  16. Yu C, Wong EM, Joo JE, Hodge AM, Makalic E, Schmidt D, Buchanan DD, Severi G, Hopper JL, English DR. Epigenetic drift association with cancer risk and survival, and modification by sex. Cancers. 2021;13(8):1881.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Horvath S. DNA methylation age of human tissues and cell types. Genome Biol. 2013;14(10):R115.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Hannum G, Guinney J, Zhao L, Zhang L, Hughes G, Sadda S, Klotzle B, Bibikova M, Fan JB, Gao Y, et al. Genome-wide methylation profiles reveal quantitative views of human aging rates. Mol Cell. 2013;49(2):359–67.

    Article  CAS  PubMed  Google Scholar 

  19. Levine ME, Lu AT, Quach A, Chen BH, Assimes TL, Bandinelli S, Hou L, Baccarelli AA, Stewart JD, Li Y, et al. An epigenetic biomarker of aging for lifespan and healthspan. Aging. 2018;10(4):573–91.

    Article  PubMed  PubMed Central  Google Scholar 

  20. Lu AT, Quach A, Wilson JG, Reiner AP, Aviv A, Raj K, Hou L, Baccarelli AA, Li Y, Stewart JD, et al. DNA methylation GrimAge strongly predicts lifespan and healthspan. Aging. 2019;11(2):303–27.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Kresovich JK, Xu Z, O’Brien KM, Weinberg CR, Sandler DP, Taylor JA. Methylation-based biological age and breast cancer risk. J Natl Cancer Inst. 2019;111:1051–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Kresovich JK, Xu Z, O’Brien KM, Weinberg CR, Sandler DP, Taylor JA. Epigenetic mortality predictors and incidence of breast cancer. Aging. 2019;11(24):11975–87.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Dugué PA, Bassett JK, Joo JE, Jung CH, Ming Wong E, Moreno-Betancur M, Schmidt D, Makalic E, Li S, Severi G, et al. DNA methylation-based biological aging and cancer risk and survival: Pooled analysis of seven prospective studies. Int J Cancer. 2018;142(8):1611–9.

    Article  PubMed  CAS  Google Scholar 

  24. Dugué PA, Bassett JK, Wong EM, Joo JE, Li S, Yu C, Schmidt DF, Makalic E, Doo NW, Buchanan DD, et al. Biological aging measures based on blood DNA methylation and risk of cancer: a prospective study. JNCI Cancer Spectr. 2021;5(1):109.

    Article  Google Scholar 

  25. Joehanes R, Just AC, Marioni RE, Pilling LC, Reynolds LM, Mandaviya PR, Guan W, Xu T, Elks CE, Aslibekyan S, et al. Epigenetic signatures of cigarette smoking. Circ Cardiovasc Genet. 2016;9(5):436–47.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Liu C, Marioni RE, Hedman ÅK, Pfeiffer L, Tsai PC, Reynolds LM, Just AC, Duan Q, Boer CG, Tanaka T, et al. A DNA methylation biomarker of alcohol consumption. Mol Psychiatry. 2016;23(2):422–33.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  27. Wahl S, Drong A, Lehne B, Loh M, Scott WR, Kunze S, Tsai PC, Ried JS, Zhang W, Yang Y, et al. Epigenome-wide association study of body mass index, and the adverse outcomes of adiposity. Nature. 2017;541(7635):81–6.

    Article  CAS  PubMed  Google Scholar 

  28. Mendelson MM, Marioni RE, Joehanes R, Liu C, Hedman AK, Aslibekyan S, Demerath EW, Guan W, Zhi D, Yao C, et al. Association of body mass index with DNA methylation and gene expression in blood cells and relations to cardiometabolic disease: a Mendelian randomization approach. PLoS Med. 2017;14(1): e1002215.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Milne RL, Fletcher AS, MacInnis RJ, Hodge AM, Hopkins AH, Bassett JK, Bruinsma FJ, Lynch BM, Dugué PA, Jayasekara H, et al. Cohort profile: the Melbourne collaborative cohort study (Health 2020). Int J Epidemiol. 2017;46(6):1757–1757i.

    Article  CAS  PubMed  Google Scholar 

  30. Prorok PC, Andriole GL, Bresalier RS, Buys SS, Chia D, Crawford ED, Fogel R, Gelmann EP, Gilbert F, Hasson MA. Design of the prostate, lung, colorectal and ovarian (PLCO) cancer screening trial. Control Clin Trials. 2000;21(6):273S-309S.

    Article  CAS  PubMed  Google Scholar 

  31. Houseman EA, Accomando WP, Koestler DC, Christensen BC, Marsit CJ, Nelson HH, Wiencke JK, Kelsey KT. DNA methylation arrays as surrogate measures of cell mixture distribution. BMC Bioinform. 2012;13(1):86.

    Article  Google Scholar 

  32. Jaffe AE, Irizarry RA. Accounting for cellular heterogeneity is critical in epigenome-wide association studies. Genome Biol. 2014;15(2):R31.

    Article  PubMed  PubMed Central  Google Scholar 

  33. McCartney DL, Hillary RF, Stevenson AJ, Ritchie SJ, Walker RM, Zhang Q, Morris SW, Bermingham ML, Campbell A, Murray AD, et al. Epigenetic prediction of complex traits and death. Genome Biol. 2018;19(1):136.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  34. Kresovich JK, Xu Z, O’Brien KM, Weinberg CR, Sandler DP, Taylor JA. Methylation-based biological age and breast cancer risk. J Natl Cancer Inst. 2019;111(10):1051–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Durso DF, Bacalini MG, Sala C, Pirazzini C, Marasco E, Bonafé M, do Valle ÍF, Gentilini D, Castellani G, Faria AMC, et al. Acceleration of leukocytes’ epigenetic age as an early tumor and sex-specific marker of breast and colorectal cancer. Oncotarget. 2017;8(14):23237–45.

    Article  PubMed  PubMed Central  Google Scholar 

  36. Hillary RF, Stevenson AJ, McCartney DL, Campbell A, Walker RM, Howard DM, Ritchie CW, Horvath S, Hayward C, McIntosh AM. Epigenetic measures of ageing predict the prevalence and incidence of leading causes of death and disease burden. Clin Epigenet. 2020;12(1):1–12.

    Article  Google Scholar 

  37. Langdon RJ, Beynon RA, Ingarfield K, Marioni RE, McCartney DL, Martin RM, Ness AR, Pawlita M, Waterboer T, Relton C, et al. Epigenetic prediction of complex traits and mortality in a cohort of individuals with oropharyngeal cancer. Clin Epigenet. 2020;12(1):58.

    Article  CAS  Google Scholar 

  38. Dugué P-A, Yu C, Hodge AM, Wong EM, Joo JE, Jung C-H, Schmidt D, Makalic E, Buchanan DD, Severi G, et al. Methylation scores for smoking, alcohol consumption, and body mass index and risk of seven types of cancer. Int J Cancer. 2021;142:691.

    Google Scholar 

  39. Tao K, Sutton J, Flanagan JM. Pan-cancer DNA methylation signature quantification of lifestyle exposures and cancer prognosis; 2021.

  40. Dugué P-A, Hodge AM, Wong EM, Joo JE, Jung C-H, Hopper JL, English DR, Giles GG, Milne RL, Southey MC. Methylation marks of prenatal exposure to maternal smoking and risk of cancer in adulthood. Int J Epidemiol. 2020;50:105–15.

    Article  Google Scholar 

  41. Kresovich JK, Xu Z, O’Brien KM, Shi M, Weinberg CR, Sandler DP, Taylor JA. Blood DNA methylation profiles improve breast cancer prediction. Mol Oncol. 2022;16(1):42–53.

    Article  PubMed  CAS  Google Scholar 

  42. Schoemaker MJ, Nichols HB, Wright LB, Brook MN, Jones ME, O’Brien KM, Adami H-O, Baglietto L, Bernstein L, Bertrand KA. Association of body mass index and age with subsequent breast cancer risk in premenopausal women. JAMA Oncol. 2018;4(11):e181771–e181771.

    Article  PubMed  PubMed Central  Google Scholar 

  43. James F, Wootton S, Jackson A, Wiseman M, Copson E, Cutress R. Obesity in breast cancer—what is the risk factor? Eur J Cancer. 2015;51(6):705–20.

    Article  CAS  PubMed  Google Scholar 

  44. Li S, Dugué PA, Baglietto L, Severi G, Wong EM, Nguyen TL, Stone J, English DR, Southey MC, Giles GG, et al. Genome-wide association study of peripheral blood DNA methylation and conventional mammographic density measures. Int J Cancer. 2019;145(7):1768–73.

    Article  CAS  PubMed  Google Scholar 

  45. Johansson A, Palli D, Masala G, Grioni S, Agnoli C, Tumino R, Giurdanella MC, Fasanelli F, Sacerdote C, Panico S, et al. Epigenome-wide association study for lifetime estrogen exposure identifies an epigenetic signature associated with breast cancer risk. Clin Epigenet. 2019;11(1):66.

    Article  CAS  Google Scholar 

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Where authors are identified as personnel of the International Agency for Research on Cancer/World Health Organization, the authors alone are responsible for the views expressed in this article and they do not necessarily represent the decisions, policy or views of the International Agency for Research on Cancer/World Health Organization.

Funding

This research was partially supported by the Intramural Research Program of the National Institutes of Health, National Cancer Institute, Division of Cancer Epidemiology and Genetics. The work performed by the Epigenomics and Mechanisms Branch at IARC was supported by grants from the Institut National du Cancer (INCa, France), the European Commission (EC) Seventh Framework Programme (FP7) Translational Cancer Research (TRANSCAN) Framework, the Fondation ARC pour la Recherche sur le Cancer (France), and la Ligue Nationale (Française) Contre le Cancer to ZH. The MCCS methylation work was supported by the National Health and Medical Research Council (grant numbers 1011618 and 1164455) and the Victorian Breast Cancer Research Consortium. The work performed at Imperial College London was funded by Breast Cancer Now and supported by the Cancer Research UK Imperial Centre, the National Institute for Health Research Imperial Biomedical Research Centre and the Ovarian Cancer Action Research Centre. MCS is a National Health and Medical Research Council Senior Research Fellow (GNT1155163).

Author information

Author notes

  1. Pierre-Antoine Dugué, Clara Bodelon and Felicia F. Chung contributed equally to this work

  2. Zdenko Herceg, Montserrat Garcia-Closas, James M. Flanagan and Melissa C. Southey contributed equally to this work

Authors and Affiliations

  1. Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC, Australia

    Pierre-Antoine Dugué, Graham G. Giles, Roger L. Milne & Melissa C. Southey

  2. Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC, Australia

    Pierre-Antoine Dugué, Dallas English, Graham G. Giles, Roger L. Milne & Melissa C. Southey

  3. Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC, Australia

    Pierre-Antoine Dugué, Dallas English, Graham G. Giles & Roger L. Milne

  4. Divison of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, USA

    Clara Bodelon, Joshua N. Sampson & Montserrat Garcia-Closas

  5. International Agency for Research On Cancer (IARC), Lyon, France

    Felicia F. Chung, Srikant Ambatipudi, Cyrille Cuenin, Veronique Chajès, Isabelle Romieu & Zdenko Herceg

  6. Department of Medical Sciences, School of Medical and Life Sciences, Sunway University, Bandar Sunway, Malaysia

    Felicia F. Chung

  7. Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, UK

    Hannah R. Brewer & James M. Flanagan

  8. AMCHSS, Sree Chitra Tirunal Institute for Medical Sciences and Technology, Thiruvananthapuram, Kerala, India

    Srikant Ambatipudi

  9. Department of Biomedical Sciences, University of Sassari, Sassari, Italy

    Giovanni Fiorito

  10. MRC Centre for Environment and Health, School of Public Health, Imperial College London, London, UK

    Giovanni Fiorito & Paolo Vineis

  11. Unit of Cancer Epidemiology, Città della Salute e Della Scienza University-Hospital, Turin, Italy

    Carlotta Sacerdote

  12. Department of Research, Epidemiology and Prevention Unit, Fondazione IRCCS Istituto Nazionale dei Tumori di Milano, Milan, MI, Italy

    Vittorio Krogh

  13. Dipartimento di Medicina Clinica e Chirurgia Federico II University, Naples, Italy

    Salvatore Panico

  14. Hyblean Association for Epidemiological Research AIRE-ONLUS, Ragusa, Italy

    Rosario Tumino

  15. Italian Institute for Genomic Medicine (IIGM), Turin, Italy

    Silvia Polidoro

  16. Department of Clinical and Experimental Medicine, University of Pisa, 56126, Pisa, Italy

    Laura Baglietto

  17. CESP UMR1018, Paris-Saclay University, UVSQ, Inserm, Gustave Roussy, Villejuif, France

    Gianluca Severi

  18. Department of Clinical Pathology, Melbourne Medical School, The University of Melbourne, Parkville, VIC, Australia

    Melissa C. Southey

Authors
  1. Pierre-Antoine Dugué
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  2. Clara Bodelon
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Contributions

All authors contributed to the conception of the study. PAD, CB, FC and JMF conducted the statistical analysis. PAD, CB, FC and JMF wrote the first draft of the manuscript. All authors reviewed the manuscript for important intellectual content and approved the final version of the manuscript.

Corresponding author

Correspondence to Pierre-Antoine Dugué.

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Ethics approval and consent to participate

The following Institutional Review Boards (IRBs) have approved the collection of different datasets, which have contributed to this study: Cancer Council Victoria’s Human Research Committee, International Agency for Research on Cancer (IARC), and the US National Cancer Institute and participating individual centers for each study.

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Not applicable.

Competing interests

The authors declare no conflict of interest.

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Supplementary Information

Additional file 1.

Supplementary Methods and Tables.

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Dugué, PA., Bodelon, C., Chung, F.F. et al. Methylation-based markers of aging and lifestyle-related factors and risk of breast cancer: a pooled analysis of four prospective studies. Breast Cancer Res 24, 59 (2022). https://doi.org/10.1186/s13058-022-01554-8

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Keywords

Breast Cancer Research

ISSN: 1465-542X

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