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

Pierre-Antoine Dugué, Clara Bodelon, Felicia F Chung, Hannah R Brewer, Srikant Ambatipudi, Joshua N Sampson, Cyrille Cuenin, Véronique Chajès, Isabelle Romieu, Giovanni Fiorito, et al.

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Pierre-Antoine Dugué, Clara Bodelon, Felicia F Chung, Hannah R Brewer, Srikant Ambatipudi, 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 Research, 2022, 24 (1), pp.59. 10.1186/s13058-022-01554-8. hal-03888900

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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

Pierre-Antoine Dugué<sup>1,2,3*†</sup>, Clara Bodelon<sup>4†</sup>, Felicia F. Chung<sup>5,6†</sup>, Hannah R. Brewer<sup>7</sup>, Srikant Ambatipudi<sup>5,8</sup>, Joshua N. Sampson<sup>4</sup>, Cyrille Cuenin<sup>5</sup>, Veronique Chajès<sup>5</sup>, Isabelle Romieu<sup>5</sup>, Giovanni Fiorito<sup>9,10</sup>, Carlotta Sacerdote<sup>11</sup>, Vittorio Krogh<sup>12</sup>, Salvatore Panico<sup>13</sup>, Rosario Tumino<sup>14</sup>, Paolo Vineis<sup>10</sup>, Silvia Polidoro<sup>15</sup>, Laura Baglietto<sup>16</sup>, Dallas English<sup>2,3</sup>, Gianluca Severi<sup>17</sup>, Graham G. Giles<sup>1,2,3</sup>, Roger L. Milne<sup>1,2,3</sup>, Zdenko Herceg<sup>5†</sup>, Montserrat Garcia-Closas<sup>4†</sup>, James M. Flanagan<sup>7†</sup> and Melissa C. Southey<sup>1,2,18†</sup>

**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

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BMC

© The Author(s) 2022. **Open Access** This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

<footnote>†Pierre-Antoine Dugué, Clara Bodelon and Felicia F. Chung contributed equally to this work</footnote>

<footnote>†Zdenko Herceg, Montserrat Garcia-Closas, James M. Flanagan and Melissa C. Southey contributed equally to this work</footnote>

<footnote>*Correspondence: pierre-antoine.dugue@monash.edu</footnote>

<footnote n="1">¹ Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC, Australia
Full list of author information is available at the end of the article</footnote>
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.

**Keywords:** Prospective study, DNA methylation, Epigenetic aging, Lifestyle, Breast cancer risk

## Introduction

Numerous studies have investigated the association of blood DNA methylation and breast cancer risk, for example, at breast cancer-specific genes [1–3], 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 [6–9] 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 [13–16].

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 con-
version, 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 analy-
sis [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
Illumina 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 sam-
ple 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 pro-
portions were estimated using the Houseman algorithm
modified by Jaffe and Irizarry [31, 32], using the R func-
tion *estimateCellCounts* implemented in *minfi*, or Hor-
vath'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 cal-
culate 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 calcu-
lated 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 chronologi-
cal age, were also computed using the online calcula-
tor. 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 compo-
sition. 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 chronologi-
cal 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-Hor-
vath 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 sub-
stantial evidence of an association with DNA methyla-
tion 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 vali-
dated in a large sample of participants of mainly Euro-
pean 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 avail-
able 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 exclu-
sion of CpGs with missing methylation values. Each
predictor was regressed on its respective risk factor—
log(BMI), log(smoking pack-years) and log(alcohol con-
sumption)—to obtain adjusted measures.

Statistical analysis

Linear regressions between each trait and its respec-
tive 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 can-
cer risk per one standard deviation (SD) increase for each
of the age acceleration, smoking, alcohol intake and BMI
methylation-based measures. Associations were also cal-
culated 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 House-
man 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><caption>**Table 1** Characteristics of study participants</caption><thead><tr><th>Participant characteristics</th><th>MCCS</th><th>EPIC-IARC</th><th>EPIC-Italy</th><th>PLCO</th></tr></thead><tbody><tr><td>Number of participants</td><td></td><td></td><td></td><td></td></tr><tr><td>Controls</td><td>408</td><td>416</td><td>248</td><td>805</td></tr><tr><td>Cases</td><td>408</td><td>416</td><td>248</td><td>583</td></tr><tr><td>Age at blood draw (cases), median (IQR)</td><td>57.4 (49.8, 62.8)</td><td>53.4 (44.7–58.9)</td><td>53.6 (48.0–57.6)</td><td>62.0 (58.3, 66.2)</td></tr><tr><td>Time to diagnosis, median (IQR)</td><td>7.7 (4.4, 11.1)</td><td>7.7 (5.0, 10.3)</td><td>6.5 (2.5, 10.6)</td><td>8.4 (5.6, 10.5)</td></tr><tr><td>ER status, n (%)</td><td></td><td></td><td></td><td></td></tr><tr><td>Positive</td><td>296 (73%)</td><td>347 (83%)</td><td>156 (63%)</td><td>411 (71%)</td></tr><tr><td>Negative</td><td>103 (25%)</td><td>69 (17%)</td><td>32 (13%)</td><td>78 (13%)</td></tr><tr><td>Missing</td><td>9 (2%)</td><td>0 (0%)</td><td>60 (24%)</td><td>94 (16%)</td></tr><tr><td>Stage, n (%)</td><td></td><td></td><td></td><td></td></tr><tr><td>I</td><td>246 (60%)</td><td>205 (49%)</td><td>71 (29%)</td><td>337 (58%)</td></tr><tr><td>II or higher</td><td>140 (35%)</td><td>143 (34%)</td><td>40 (16%)</td><td>245 (42%)</td></tr><tr><td>Missing</td><td>22 (5%)</td><td>68 (16%)</td><td>137 (55%)</td><td>1 (0.1%)</td></tr><tr><td>Smoking, n (%)</td><td></td><td></td><td></td><td></td></tr><tr><td>Current</td><td>Cases 27 (7%)</td><td>Controls 88 (21%)</td><td>Cases 94 (23%)</td><td>Controls 47 (8%)</td></tr><tr><td>Former</td><td>94 (23%)</td><td>113 (28%)</td><td>91 (22%)</td><td>50 (20%)</td></tr><tr><td>Never</td><td>287 (70%)</td><td>268 (66%)</td><td>231 (56%)</td><td>141 (57%)</td></tr><tr><td>Smoking pack-years, median (IQR)</td><td>0 (0, 4.0)</td><td>0 (0, 5.7)</td><td>0 (0, 6.5)</td><td>0 (0, 5.4)</td></tr><tr><td>Alcohol intake (g/day), median (IQR)</td><td>0 (0, 8.6)</td><td>0 (0, 9.5)</td><td>3.4 (0.4, 11.8)</td><td>2.8 (0,13.4)</td></tr><tr><td>Body mass index (kg/m²), median (IQR)</td><td>26 (23, 30)</td><td>26 (23, 29)</td><td>25 (23, 28)</td><td>25 (23, 28)</td></tr></tbody></table>

MCCS Melbourne Collaborative Cohort Study; EPIC European Prospective Investigation into Cancer and Nutrition; IARC International Agency for Research on Cancer; PLCO Prostate, Lung, Colorectal and Ovarian (PLCO) Cancer Screening Trial; IQR interquartile range; ER oestrogen receptor
<table><caption>**Table 2** Description of methylation-based measures</caption><thead><tr><th rowspan="2">Methylation-based measures</th><th colspan="2">MCCS</th><th colspan="2">EPIC-IARC</th><th colspan="2">EPIC-Italy</th><th colspan="2">PLCO</th></tr><tr><th>Median (IQR)</th><th>R<sup>2,*</sup> (%)</th><th>Median (IQR)</th><th>R<sup>2,*</sup> (%)</th><th>Median (IQR)</th><th>R<sup>2,*</sup> (%)</th><th>Median (IQR)</th><th>R<sup>2,*</sup> (%)</th></tr></thead><tbody><tr><td>Horvath age</td><td>54 (47; 60)</td><td>49</td><td>51 (44; 57)</td><td>60</td><td>52 (46; 57)</td><td>64</td><td>65 (62; 69)</td><td>39</td></tr><tr><td>Hannum age</td><td>62 (55; 68)</td><td>59</td><td>53 (46; 60)</td><td>64</td><td>59 (53.6; 62.9)</td><td>72</td><td>56 (52; 59)</td><td>48</td></tr><tr><td>PhenoAge</td><td>54 (46; 61)</td><td>39</td><td>44 (36; 51)</td><td>50</td><td>42 (35; 48)</td><td>56</td><td>51 (47; 56)</td><td>32</td></tr><tr><td>GrimAge</td><td>53 (48; 57)</td><td>66</td><td>52 (47; 57)</td><td>69</td><td>59 (55; 63)</td><td>62</td><td>46 (42; 50)</td><td>50</td></tr><tr><td>BMI score</td><td>0.0 (−0.6; 0.7)</td><td>16</td><td>0.0 (−0.7; 0.7)</td><td>14</td><td>−0.1 (−0.6; 0.7)</td><td>18</td><td>0.0 (−0.7; 0.6)</td><td>22</td></tr><tr><td>Smoking score</td><td>−0.2 (−0.6; 0.3)</td><td>41</td><td>−0.4 (−0.6; 0.1)</td><td>44</td><td>−0.4 (−0.6; 0.2)</td><td>54</td><td>−0.3 (−0.6; 0.2)</td><td>41</td></tr><tr><td>Alcohol score</td><td>−0.1 (−0.6; 0.6)</td><td>3</td><td>−0.1 (−0.7; 0.6)</td><td>7</td><td>−0.1 (−0.6; 0.6)</td><td>3</td><td>−0.1 (−0.6; 0.5)</td><td>9</td></tr></tbody></table>

MCCS Melbourne Collaborative Cohort Study; EPIC European Prospective Investigation into Cancer and Nutrition; IARC International Agency for Research on Cancer; PLCO Prostate, Lung, Colorectal and Ovarian (PLCO) Cancer Screening Trial; IQR Interquartile range

*Variance explained of age, BMI, smoking and alcohol consumption by the corresponding methylation-based measures

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).

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

<table><caption>**Table 3** Odds ratios<sup>a</sup> for the association between methylation-based measures of breast cancer risk factors and risk of breast cancer</caption><thead><tr><th>Methylation-based measures</th><th>MCCS<br>OR [95%CI]</th><th>EPIC-IARC<br>OR [95%CI]</th><th>EPIC-Italy<br>OR [95%CI]</th><th>PLCO<br>OR [95%CI]</th><th>l<sup>2</sup> (%)</th><th>Pooled<br>OR [95%CI]</th><th>P</th></tr></thead><tbody><tr><td><strong>Epigenetic aging</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>AA-Horvath<sup>b</sup></td><td>0.95 [0.81–1.10]</td><td>1.22 [1.02–1.47]</td><td>0.89 [0.73–1.08]</td><td>1.05 [0.93–1.18]</td><td>57</td><td><strong>1.02 [0.95–1.10]</strong></td><td>0.59</td></tr><tr><td>IEAA-Horvath<sup>b</sup></td><td>0.96 [0.83–1.10]</td><td>1.23 [1.03–1.47]</td><td>0.88 [0.72–1.06]</td><td>1.07 [0.95–1.20]</td><td>62</td><td><strong>1.03 [0.96–1.11]</strong></td><td>0.40</td></tr><tr><td>EEAA<sup>b</sup></td><td>0.99 [0.84–1.18]</td><td>1.15 [0.94–1.41]</td><td>0.86 [0.70–1.06]</td><td>1.06 [0.92–1.22]</td><td>31</td><td><strong>1.02 [0.93–1.11]</strong></td><td>0.67</td></tr><tr><td>AA-Hannum<sup>b</sup></td><td>0.97 [0.83–1.14]</td><td>1.19 [0.97–1.45]</td><td>0.86 [0.70–1.06]</td><td>1.09 [0.96–1.24]</td><td>51</td><td><strong>1.03 [0.95–1.12]</strong></td><td>0.43</td></tr><tr><td>IEAA-Hannum<sup>b</sup></td><td>0.98 [0.84–1.13]</td><td>1.18 [0.98–1.42]</td><td>0.88 [0.72–1.08]</td><td>1.08 [0.96–1.22]</td><td>45</td><td><strong>1.04 [0.96–1.12]</strong></td><td>0.34</td></tr><tr><td>PhenoAge</td><td>0.97 [0.83–1.14]</td><td>1.12 [0.94–1.34]</td><td>0.89 [0.73–1.10]</td><td>1.03 [0.91–1.17]</td><td>4</td><td><strong>1.01 [0.94–1.09]</strong></td><td>0.75</td></tr><tr><td>GrimAge</td><td>1.03 [0.86–1.25]</td><td>1.08 [0.90–1.30]</td><td>0.94 [0.79–1.13]</td><td>1.05 [0.91–1.21]</td><td>0</td><td><strong>1.03 [0.94–1.12]</strong></td><td>0.53</td></tr><tr><td><strong>Lifestyle-related factors</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>BMI methylation score</td><td>1.17 [1.01–1.36]</td><td>1.06 [0.92–1.23]</td><td>1.25 [1.03–1.52]</td><td>1.02 [0.91–1.15]</td><td>26</td><td><strong>1.10 [1.02–1.18]</strong></td><td>0.01</td></tr><tr><td>Adjusted BMI score<sup>c</sup></td><td>1.11 [0.96–1.28]</td><td>1.02 [0.88–1.17]</td><td>1.27 [1.04–1.55]</td><td>1.06 [0.94–1.19]</td><td>12</td><td><strong>1.09 [1.01–1.17]</strong></td><td>0.02</td></tr><tr><td>Smoking methylation score</td><td>0.98 [0.84–1.14]</td><td>0.95 [0.82–1.11]</td><td>1.11 [0.90–1.35]</td><td>1.13 [1.00–1.27]</td><td>24</td><td><strong>1.04 [0.97–1.12]</strong></td><td>0.25</td></tr><tr><td>Adjusted smoking score<sup>c</sup></td><td>1.01 [0.87–1.17]</td><td>0.94 [0.81–1.09]</td><td>1.13 [0.94–1.34]</td><td>1.08 [0.97–1.22]</td><td>6</td><td><strong>1.04 [0.97–1.12]</strong></td><td>0.29</td></tr><tr><td>Alcohol methylation score</td><td>0.95 [0.82–1.11]</td><td>1.01 [0.87–1.17]</td><td>0.83 [0.67–1.03]</td><td>1.07 [0.95–1.20]</td><td>32</td><td><strong>1.00 [0.93–1.07]</strong></td><td>0.91</td></tr><tr><td>Adjusted alcohol score<sup>c</sup></td><td>0.95 [0.82–1.11]</td><td>0.96 [0.83–1.12]</td><td>0.85 [0.69–1.05]</td><td>1.03 [0.91–1.16]</td><td>0</td><td><strong>0.97 [0.90–1.04]</strong></td><td>0.42</td></tr></tbody></table>

Bold values indicate results pooled across the four studies

<footnote><sup>a</sup> Adjusting for white blood cell proportions, BMI, smoking and alcohol consumption</footnote>

<footnote><sup>b</sup> These models were not adjusted for white blood cell proportions because these are integrated in the IEAA and EEAA measures</footnote>

<footnote><sup>c</sup> Residuals from the regression of the scores on the log of their respective lifestyle variables</footnote>
in the pooled analysis with an OR of 1.10 per SD (95%CI: 1.02–1.18), with little heterogeneity across cohorts ($I^2$=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 ($I^2$=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).

## 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,

<table><caption>**Table 4** Odds ratios<sup>a</sup> (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)</caption><thead><tr><th></th><th>AA-Horvath<sup>b</sup><br>OR [95%CI]</th><th>AA-Hannum<sup>b</sup><br>OR [95%CI]</th><th>PhenoAge<sup>b</sup><br>OR [95%CI]</th><th>GrimAge<sup>b</sup><br>OR [95%CI]</th><th>Adj. BMI score<sup>c</sup><br>OR [95%CI]</th><th>Adj. smoking score<sup>c</sup><br>OR [95%CI]</th><th>Adj. alcohol score<sup>c</sup><br>OR [95%CI]</th></tr></thead><tbody><tr><td>Time to diagnosis</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>&lt; 5 years</td><td>1.01 [0.88–1.16]</td><td>0.98 [0.84–1.13]</td><td>1.01 [0.88–1.17]</td><td>1.04 [0.92–1.19]</td><td>1.15 [1.01–1.32]</td><td>0.99 [0.87–1.13]</td><td>1.01 [0.88–1.15]</td></tr><tr><td>≥ 5 years</td><td>1.02 [0.94–1.11]</td><td>1.06 [0.97–1.16]</td><td>1.03 [0.95–1.13]</td><td>1.05 [0.96–1.14]</td><td>1.08 [1.00–1.17]</td><td>1.06 [0.98–1.14]</td><td>0.96 [0.88–1.05]</td></tr><tr><td>Age at blood draw</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>&lt; 50</td><td>1.12 [0.92–1.36]</td><td>1.08 [0.89–1.32]</td><td>1.02 [0.84–1.23]</td><td>1.10 [0.93–1.30]</td><td>1.10 [0.93–1.30]</td><td>0.99 [0.85–1.14]</td><td>0.96 [0.81–1.14]</td></tr><tr><td>≥ 50</td><td>1.00 [0.92–1.09]</td><td>1.03 [0.94–1.12]</td><td>1.03 [0.95–1.12]</td><td>1.03 [0.95–1.11]</td><td>1.10 [1.02–1.19]</td><td>1.04 [0.96–1.13]</td><td>0.96 [0.89–1.05]</td></tr><tr><td>ER positivity status</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>ER positive</td><td>1.06 [0.98–1.16]</td><td>1.04 [0.95–1.14]</td><td>1.01 [0.93–1.10]</td><td>1.03 [0.95–1.12]</td><td>1.05 [0.97–1.14]</td><td>1.03 [0.95–1.12]</td><td>0.97 [0.89–1.06]</td></tr><tr><td>ER negative</td><td>0.87 [0.73–1.04]</td><td>0.93 [0.77–1.12]</td><td>1.02 [0.85–1.23]</td><td>1.18 [1.00–1.40]</td><td>1.15 [0.97–1.37]</td><td>1.05 [0.89–1.23]</td><td>0.92 [0.76–1.10]</td></tr><tr><td>Stage</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>I</td><td>1.05 [0.95–1.16]</td><td>1.04 [0.94–1.16]</td><td>1.02 [0.92–1.12]</td><td>1.07 [0.98–1.18]</td><td>1.09 [0.99–1.19]</td><td>1.03 [0.94–1.14]</td><td>0.96 [0.87–1.06]</td></tr><tr><td>II or higher</td><td>1.00 [0.88–1.15]</td><td>1.08 [0.93–1.25]</td><td>1.04 [0.91–1.20]</td><td>1.08 [0.94–1.24]</td><td>1.08 [0.95–1.23]</td><td>1.12 [0.98–1.28]</td><td>1.02 [0.89–1.17]</td></tr></tbody></table>

<footnote><sup>a</sup> Adjusting for white blood cell proportions, except for AA-Horvath and AA-Hannum</footnote>

<footnote><sup>b</sup> Regressed on age</footnote>

<footnote><sup>c</sup> Regressed on log(BMI), log-pack-years) and log(alcohol in grams/day), respectively</footnote>
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 sub-
stantially 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 neverthe-
less 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 Ital-
ian 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 epige-
netic aging measured in blood and breast cancer risk.

There has not been to our knowledge any study exam-
ining 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 data-
sets 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 meas-
ures 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 can-
cer 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 methyla-
tion-based measures of biological aging and physiological
characteristics and methylation values at 100 individual
CpGs (i.e. using altogether methylation values at thou-
sands 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 asso-
ciation 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 improve-
ments 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 con-
sumption or smoking are strong risk factors for breast
cancer. Previous studies have generally found weak to
moderate associations [33, 37, 38], except for lung can-
cer in the MCCS [38], for which the effect of smoking is
dramatic. We had hypothesised that methylation predic-
tors of BMI, alcohol and smoking could contain more
information about lifestyle than the measured risk fac-
tors—for example, exposures accumulated over the life-
time, in particular, during sensitive periods such as early
life or the periconceptional period, which could be bet-
ter captured by DNA methylation compared with ques-
tionnaires at older ages. BMI has consistently been found
to be positively associated with risk of breast cancer for
postmenopausal women and negatively for pre-meno-
pausal women [42]; we did not observe this using methyl-
ation 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 con-
vincing evidence that they are strongly associated with
blood DNA methylation changes, e.g. for mammographic
density [44] or lifetime oestrogen exposure [45]. Addi-
tional 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.

## Supplementary Information

The online version contains supplementary material available at https://doi.org/10.1186/s13058-022-01554-8.

Additional file 1. Supplementary Methods and Tables.

## Disclaimer

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.

## Author 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.

## 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).

## 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.

## Declarations

### 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.

### Consent for publication

Not applicable.

### Competing interests

The authors declare no conflict of interest.

### Author details

¹Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC, Australia. ²Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC, Australia. ³Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC, Australia. ⁴Divison of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, USA. ⁵International Agency for Research On Cancer (IARC), Lyon, France. ⁶Department of Medical Sciences, School of Medical and Life Sciences, Sunway University, Bandar Sunway, Malaysia. ⁷Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, UK. ⁸AMCHSS, Sree Chitra Tirunal Institute for Medical Sciences and Technology, Thiruvananthapuram, Kerala, India. ⁹Department of Biomedical Sciences, University of Sassari, Sassari, Italy. ¹⁰MRC Centre for Environment and Health, School of Public Health, Imperial College London, London, UK. ¹¹Unit of Cancer Epidemiology, Città della Salute e Della Scienza University-Hospital, Turin, Italy. ¹²Department of Research, Epidemiology and Prevention Unit, Fondazione IRCCS Istituto Nazionale dei Tumori di Milano, Milan, MI, Italy. ¹³Dipartimento di Medicina Clinica e Chirurgia Federico II University, Naples, Italy. ¹⁴Hyblean Association for Epidemiological Research AIRE-ONLUS, Ragusa, Italy. ¹⁵Italian Institute for Genomic Medicine (IIGM), Turin, Italy. ¹⁶Department of Clinical and Experimental Medicine, University of Pisa, 56126 Pisa, Italy. ¹⁷CESP UMR1018, Paris-Saclay University, UVSQ, Inserm, Gustave Roussy, Villejuif, France. ¹⁸Department of Clinical Pathology, Melbourne Medical School, The University of Melbourne, Parkville, VIC, Australia.

Received: 13 May 2022 Accepted: 12 August 2022

Published online: 06 September 2022

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