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Relationships between Global DNA Methylation in Circulating White Blood Cells and Breast Cancer Risk Factors.

1. Introduction

A CpG site, a cytosine followed by a guanine, has the potential to be methylated, and measuring 5-methyl-2' deoxycytidine (5-mdC) content across the genome by liquid chromatography/mass spectrometry (LC/MS) can provide an overall measure of genome-wide DNA methylation levels. Repetitive sequences of the genome such as LINE-1 and Alu contain up to half of all DNA methylation in the genome [1]. Thus, measuring DNA methylation levels in LINE-1 or Alu repetitive elements by pyrosequencing or Methyl Light is often used as a surrogate higher-throughput approach to assess genome-wide methylation [2]. Genome instability has been associated with DNA hypomethylation and such global loss of methylation is common in breast tumor tissue [3-7].

There is some evidence that peripheral white blood cell (WBC) DNA contains epigenetic information that can be used to assess an individual's risk of breast cancer. In a case-control study of breast cancer of 179 cases and 180 controls, Choi and colleagues observed a nearly threefold increase in risk among women in the lowest tertile of total 5-mdC levels in WBC DNA compared to women in the highest tertile [1]. In the NIEHS sister case-cohort study of 294 cases and 646 noncases in which the mean time between blood collection and breast cancer diagnosis was 15 months [8], LINE-1 methylation percentage in WBC DNA was also inversely associated with the risk of breast cancer, with a nearly twofold increased risk observed among women in the lowest quartile compared with those in the highest quartile. However, Brennan and colleagues reported no association between LINE-1 WBC methylation and breast cancer risk in three prospective nested case-control studies [9]. Several other case-control studies, ranging in the number of breast cancer cases from 19 to 1064, found no association between LINE-1 methylation and breast cancer risk [1, 10-12] or between Alu methylation and breast cancer risk [12,13].

The LUminometric Methylation Assay (LUMA) measures levels of 5-mdC in a specific CmCGG motif which is found both in promoter regions of the genome and in repetitive elements [11]. Interestingly, one case-control study reported a twofold increase in risk of breast cancer among women with higher 5-mdC content compared to those with lower levels measured by LUMA. Another study reported no association [14] and a third study reported a strong inverse association between increasing tertiles of LUMA methylation and breast cancer risk [15].

It is not yet clear whether WBC DNA global methylation is associated with breast cancer risk [16]. If there is an association, one possible explanation is that the association represents environmental and lifestyle determinants of breast cancer that influence both DNA methylation and breast cancer risk. An alternative possibility is that, in response to very early breast cancer, a new clone of circulating lymphocytes arises that alters white blood cell DNA methylation [17]. If WBC DNA methylation is a marker of exposure associated with breast cancer risk, rather than a marker of early disease, it is reasonable to expect that white blood cell DNA methylation patterns would be more likely to be correlated with hormonal and other established or suspected risk factors for breast cancer. Terry and colleagues [18] reviewed literature up to 2011 on the relation between WBC DNA methylation patterns and a number of cancer risk factors. As the literature has expanded substantially, we updated the review, focusing on four demographic factors (age, sex, race/ethnicity, and education), three lifestyle factors (alcohol, smoking, and physical activity), three dietary factors (BMI, vegetable intake, and fruit intake), and eight health history and reproductive factors (menopause status, fetal birth weight, family history of breast cancer, age at menarche, age at first birth, parity, hormone replacement therapy, and endogenous hormones) that have been associated with breast cancer [19]. We also included folate in our review because although results have been mixed for breast cancer, folate is plausibly linked to DNA methylation [20]. We examined the relationships between these breast cancer risk factors and three markers of global DNA methylation: LINE-1, 5-mdC, and Alu. Our review comprises literature published through April 2016 and includes over 30 new studies that were not included in the 2011 review [18].

2. Methods

A literature search was conducted using Pubmed up to April 1, 2016. Searches were performed using combinations of relevant outcomes such as "WBC methylation," "blood methylation," "blood LINE-1 methylation," and a comprehensive list of known and suspected breast cancer risk factors such as "diet," "physical activity," and "menopause." Boolean operators "and" and "or" were used whenever appropriate. Titles and abstracts were screened to determine relevancy by three independent reviewers. Additionally, bibliographies of select reviews were screened to ensure capture of all relevant information and ideas. If relevancy could not be determined from the abstract, the full text was retrieved to ensure comprehensive capture.

A study was included if it was primary research, published in English, and contained relevant results on any risk factor and blood DNA methylation outcomes. Studies were included that had both men and women due to the limited number of studies performed only in women. Studies were only included if their data were based on populations of nondiseased individuals.

3. Results

Table 1 shows the number of studies reporting associations between global WBC DNA methylation and demographic, lifestyle, dietary, and reproductive factors for each of three markers (i.e., LINE-1, Alu, and 5-mdC). Overall, the vast majority of reports in the literature have focused on LINE-1. For example, 21 studies examined the association between age and LINE-1 but only four studies examined age and 5-mdC. There were ten or more studies that each examined the association between LINE-1 and alcohol, smoking, body mass index, vegetables, and folate. Fewer studies were available for Alu and 5-mdC and for reproductive risk factors.

3.1. Demographic Factors

3.1.1. Age. As shown in Table 2, twenty of the twenty-one studies examining LINE-1 reported no significant association between age and LINE-1 methylation levels [11, 21-38]. Only one study reported a significant association between increasing age and higher LINE-1 methylation levels [29]. Three of the six studies examining Alu methylation found no significant association with age [38, 41, 42], while the other three studies reported a significant association between increasing age and decreasing Alu methylation [22, 39, 40]. Two of four studies examining 5-mdC levels did not find a significant association with age [1, 44], while two other studies reported a statistically significant association between increasing age and decreasing 5-mdC levels [39, 43]. In summary, of the 31 studies with data on age and estimates of global DNA methylation no relationship was reported for 25 of the studies, a significant inverse relationship was reported for five studies, and a positive relationship was found in only one study.

3.1.2. Sex. As shown in Table 2, eleven of the seventeen studies reported statistically significant higher LINE-1 levels in males than in females [25, 28-30, 32, 34-36, 42, 45, 46], while the other six studies found no statistically significant association between sex and LINE-1 levels [23, 27, 33, 37, 38, 47]. Two studies found no significant association between sex and Alu methylation [38, 40] while two other studies found a significant association for higher Alu methylation in males than in females [42, 48]. One study showed a significant association for higher 5-mdC levels in males than in females [43]. In summary, of the 22 studies with data on sex and estimates of global DNA methylation no relationship was reported for eight of the studies, and significant inverse relationship was reported for fourteen studies.

3.1.3. Race/Ethnicity. Five studies investigated the association between LINE-1 methylation and race/ethnicity (Table 2). Non-Hispanic Blacks had a significantly lower LINE-1 level compared to non-Hispanic Whites [36] in one study, whereas the reverse was observed in two studies [28, 34]. Two other studies showed no significant association between race/ethnicity and LINE-1 levels [11, 37]. One study showed no significant association between race/ethnicity and 5mdC levels [1]. In summary, of the six studies with data on race/ethnicity and estimates of global DNA methylation no relationship was reported for three of the studies, non-Hispanic Blacks had a significantly lower global DNA methylation compared to non-Hispanic Whites in one of the studies, and the inverse relationship was reported for two of the studies.

3.1.4. Education. As shown in Table 2, all six studies examining LINE-1 that included education as a risk factor reported no significant association between the levels of education attained and LINE-1 levels [21, 27, 29, 34, 36]. None of the studies examining Alu methylation included education as a risk factor. Only one 5-mdC study included education and it reported no significant association between the levels of education attained and 5-mdC levels [1]. In summary, of the seven studies with data on education and estimates of global DNA methylation no relationship was reported for any of the seven studies.

3.2. Lifestyle Factors

3.2.1. Physical Activity. Five studies have investigated the association between physical activity and LINE-1 levels (Table 3). Four studies found no significant difference between physical activity and LINE-1 levels [24, 34, 37, 50] whereas one study reported that higher physical activity was associated with higher DNA methylation levels [49]. No studies examined the association between physical activity and Alu or 5-mdC. In summary, of the five studies with data on physical activity and estimates of global DNA methylation no relationship was reported for four of the studies and a positive relationship was found in one study.

3.2.2. Alcohol. As shown in Table 3, thirteen studies examined LINE-1 methylation and alcohol consumption. None of the thirteen studies reported a significant relationship between alcohol and LINE-1 levels [11, 21, 27-29, 32-34, 36-38, 47]. Three studies found no significant association between alcohol and Alu methylation [38, 40, 41]. Additionally, of the only study that examined 5-mdC and alcohol consumption, there was no significant association between alcohol and 5-mdC levels [1]. In summary, of the 17 studies with data on alcohol and estimates of global DNA methylation no relationship was reported for any of the 17 studies.

3.2.3. Smoking. As shown in Table 3, sixteen studies examined the relationship between LINE-1 and smoking and all but one ofthe studies reported no significant association between LINE-1 level and smoking habits [11, 21, 26-30, 32-36, 38, 47]. All four studies examining Alu found no association between smoking and Alu levels [38, 40-42], and both studies involving 5-mdC found no significant association between smoking and 5-mdC levels [1, 44]. In summary, of the 22 studies with data on smoking and estimates of global DNA methylation no relationship was reported for 21 of the studies and a significant inverse relationship was reported for one study with smokers having lower DNA methylation.

3.3. Dietary Factors

3.3.1. BMI. A total of thirteen studies have examined the relationship between BMI and LINE-1 levels (Table 3). Twelve studies reported no relationship [21, 24, 26, 29-31, 33, 34, 36-38, 47] while one study found that a higher BMI was statistically significantly associated with a lower LINE-1 level [29]. Two studies found no significant association between BMI and Alu methylation [38, 40] while one study found that a higher BMI was significantly associated with a lower Alu methylation level [41]. One study found no relationship between BMI and 5-mdC levels [1]. In summary, of the 17 studies with data on BMI and estimates of global DNA methylation no relationship was reported for 15 ofthe studies, and significant inverse relationship was reported for two studies.

3.3.2. Vegetables. As shown in Table 3, eight studies conducted found no relationship between vegetable intake and LINE-1 levels [21, 24, 27, 29, 30, 37, 47]. Two other studies showed a significant association between lower vegetable intake and lower LINE-1 methylation [29, 46]. One study found a significant association between higher adherence to a Mediterranean diet and lower LINE-1 levels [51]. In summary, of the 11 studies with data on vegetables and estimates of global DNA methylation no relationship was reported for eight of the studies, and significant positive relationship was reported for three studies.

3.3.3. Fruit. Six studies investigated the relationship between fruit intake and global white blood cell DNA methylation levels (Table 3). Five studies found no significant association between levels of fruit intake and LINE-1 levels [27, 29, 37, 47]. One study found that, in women, there was a significant association between lower fruit intake and lower LINE-1 levels [21]. In summary, of the six studies with data on fruit and estimates of global DNA methylation no relationship was reported for five of the studies, and significant positive relationship was reported for one study in women.

3.3.4. Folate. As shown in Table 3, ten studies reported no significant relationship between dietary folate intake and LINE-1 levels [11, 26-29, 34, 36, 44, 47]. Two studies reported a statistically significant positive correlation between higher blood folate levels and higher LINE-1 levels [37,52]. However, in one of the same studies, folate intake from natural foods and total dietary folate equivalents were not found to be associated with higher LINE-1 levels [37]. Another study reported that women with a folate deficiency had a statistically significantly lower LINE-1 level [21]. In summary, of the 13 studies with data on folate and global DNA methylation estimates no relationship was reported for ten of the studies and a significant positive relationship was reported for three of the studies.

3.4. Health History and Reproductive Factors

3.4.1. Menopause Status. As shown in Table 3, one study examined the relationship between menopausal status and LINE-1 methylation and did not find a significant association [11]. Another study did not find a significant association between menopausal status and 5-mdC levels [1].

3.4.2. Fetal Birthweight. A cross-sectional study investigated the relationship between fetal birthweight and LINE-1 levels and found a significant association between low (<2500 g) or high (4000+ g) birthweight and lower LINE-1 levels of the newborn [53] (Table 3).

3.4.3. Family History of Breast Cancer. Four studies reported no relationship between family history of breast cancer and LINE-1 levels [9, 11, 54, 55] (Table 3). Family history of breast cancer was unrelated to 5-mdC levels in another study [1]. However, one study did find a relationship between family history of breast cancer and lower Alu levels [55]. In summary, of the six studies with data on family history and estimates of global DNA methylation no relationship was reported for five of the studies, and significant inverse relationship was reported for one study.

3.4.4. Age at Menarche. One study found no association between the age at menarche and 5-mdC level [1] (Table 3).

3.4.5. Age at First Birth/Parity. As seen in Table 3, one study did not find a significant association between the age at first live birth or parity, and 5-mdC level [1].

3.4.6. Endogenous Hormones/Hormone Use. All four studies found no statistically significant association between LINE1 levels and sex hormone levels [56, 57] or between LINE1 levels and phase of the menstrual cycle [25] (Table 3). The only study that evaluated 5-mdC levels did not find a significant association between 5-mdC and hormone use [1].

4. Discussion

There was reasonably consistent evidence across studies that males have higher levels of global methylation in WBC DNA than females. There was little evidence across studies that age was associated with global methylation in WBC DNA but the populations studied were generally restricted to older adults. Age has been reported to be associated with WBC DNA LINE-1 methylation in a study that evaluated epigenetic changes throughout the lifetime of monozygotic twins [39]. None of the other demographic, lifestyle, dietary, or other risk factors were consistently associated with global WBC DNA methylation.

There are several factors that warrant consideration in interpreting the existing data on the associations between WBC DNA methylation and breast cancer risk factors. Nearly all the published studies used a composite of DNA from different subtypes of WBCs. As previously noted by others [18, 58], DNA methylation can vary by WBC subtype and the distribution of WBC subtypes varies among individuals, which could possibly obscure associations. In addition, the type and method of assessing WBC DNA methylation differed across studies, which could potentially contribute to variation in results across studies. Another possible explanation for the general lack of association with breast cancer risk factors is that the assays used may not be optimal. However, the fact that global WBC DNA methylation levels appear to be slightly lower among women than men when measured by any of the three assays tends to suggest that laboratory measurement error is not the entire explanation. Finally, studies are generally cross-sectional in design [18], and for some of the risk factors examined the number of studies was quite limited. Overall, however, it seems unlikely that these considerations account for the consistently null findings observed.

4.1. Conclusion. In summary, with the exception of sex, there is very little evidence that the wide range of breast cancer risk factors we examined (demographic, lifestyle, dietary, and health conditions) were associated with global WBC DNA methylation markers including LINE-1, 5-mdC, and Alu. Although the possibility that global DNA methylation reflects a novel breast cancer risk factor cannot be ruled out on the basis of these findings, a plausible implication of the observed lack of association between global WBC DNA methylation and most known or suspected breast cancer risk factors is that the association between global WBC DNA methylation and breast cancer, if it exists, is due to a disease effect [16, 59].

https://doi.org/10.1155/2017/2705860

Conflicts of Interest

The authors declare that there are no conflicts of interest regarding the publication of this paper.

Acknowledgments

Research reported in this publication was supported in part by the National Cancer Institute of the National Institutes of Health under Award no. R15CA170111.

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Nayha Chopra-Tandon, (1) Haotian Wu, (2) Kathleen F. Arcaro, (3) and Susan R. Sturgeon (1)

(1) Department of Biostatistics and Epidemiology, School of Public Health and Health Sciences, University of Massachusetts, Amherst, MA, USA

(2) Department of Environmental Health Sciences, School of Public Health and Health Sciences, University of Massachusetts, Amherst, MA, USA

(3) Department of Veterinary and Animal Science, College of Natural Sciences, University of Massachusetts, Amherst, MA, USA

Correspondence should be addressed to Nayha Chopra-Tandon; nchoprat@umass.edu

Received 22 October 2016; Revised 26 February 2017; Accepted 14 March 2017; Published 6 April 2017

Academic Editor: Yun-Ling Zheng
TABLE 1: Summary of number of reports by risk factor and global
methylation measure.

Factors                Total # of reports   LINE-1   Alu   5-mdC

Demographic Factors
  Age                          31             21      6      4
  Sex                          22             17      4      1
  Race/ethnicity               6              5       0      1
  Education                    7              6       0      1
Lifestyle factors
  Physical activity            5              5       0      0
  Alcohol                      17             13      3      1
  Smoking                      22             16      4      2
Dietary factors
  BMI                          17             13      3      1
  Vegetables                   11             11      0      0
  Fruit                        6              6       0      0
  Folate                       13             12      0      1
Reproductive factors
  Menopause status             2              1       0      1
  Fetal birthweight            1              1       0      0
  Family history of            6              4       1      1
     breast cancer
  Age at menarche              1              0       0      1
  Age at first birth           1              0       0      1
  Parity                       1              0       0      1
  Hormones                     4              3       0      1

TABLE 2: Study Findings for demographic factors.

Authors                     Methylation    Measurement method
                               Type

Age

Agodi et al., 2015 [21]       LINE-1         Pyrosequencing
Bollati et al.,               LINE-1         Pyrosequencing
  2009 [22]
Chalitchagorn                 LINE-1            COBRA PCR
  et al., 2004 [23]
Duggan et al., 2014 [24]      LINE-1         Pyrosequencing
El-Maarri et al.,             LINE-1      Pyrosequencing, SIRPH
  2011 [25]
Gomes et al., 2012 [26]       LINE-1              ELISA
Hou et al., 2010 [27]         LINE-1         Pyrosequencing
Hsiung et al., 2007 [28]      LINE-1            COBRA PCR
Karami et al., 2015 [29]      LINE-1         Pyrosequencing
Liao et al., 2011 [30]        LINE-1         Pyrosequencing
Marques-Rocha et              LINE-1             MS-HRM
  al., 2016 [31]
Mirabello et al.,             LINE-1         Pyrosequencing
  2010 [32]
Pearce et al., 2012 [33]      LINE-1         Pyrosequencing
Perng et al., 2014 [34]       LINE-1         Pyrosequencing
Wilhelm et al.,               LINE-1         Pyrosequencing
  2010 [35]
Xu et al., 2012 [11]          LINE-1         Pyrosequencing
Zhang et al., 2011 [36]       LINE-1         Pyrosequencing
Zhang et al., 2012 [37]       LINE-1         Pyrosequencing
Zhu et al., 2012 [38]         LINE-1         Pyrosequencing
Bollati et al., 2009 [22]       Alu          Pyrosequencing
Fraga et al., 2005 [39]         Alu       Total 5-mdC content:
                                              HPCE Sequence
                                           specific: bisulfite
                                               sequencing
Kim et al., 2010 [40]           Alu          Pyrosequencing
Na et al., 2014 [41]            Alu          Pyrosequencing
Rusiecki et al.,                Alu          Pyrosequencing
  2008 [42]
Zhu et al., 2012 [38]           Alu          Pyrosequencing
Choi et al., 2009 [1]          5-mdC          LC/ESI-MS/MS
Fraga et al., 2005 [39]        5-mdC      Total 5-mdC content:
                                              HPCE Sequence
                                           specific: bisulfite
                                               sequencing
Fuke et al., 2004 [43]         5-mdC              HPLC
Moore et al., 2008 [44]        5-mdC       HPCE, Hpall digest,
                                              densitometry

Sex

Andreotti et al.,             LINE-1         Pyrosequencing
  2014 [45]
Cash et al., 2012 [46]        LINE-1         Pyrosequencing
Chalitchagorn et              LINE-1            COBRA PCR
  al., 2004 [23]
El-Maarri et al.,             LINE-1      Pyrosequencing, SIRPH
  2011 [25]
Hou et al., 2010 [27]         LINE-1         Pyrosequencing
Hsiung et al., 2007 [28]      LINE-1            Cobra PCR
Karami et al., 2015 [29]      LINE-1         Pyrosequencing
Liao et al., 2011 [30]        LINE-1         Pyrosequencing
Mirabello et al.,             LINE-1         Pyrosequencing
  2010 [32]
Pearce et al., 2012 [33]      LINE-1         Pyrosequencing
Perng et al., 2014 [34]       LINE-1         Pyrosequencing
Rusiecki et al.,              LINE-1         Pyrosequencing
  2008 [42]
Tajuddin et al.,              LINE-1         Pyrosequencing
  2013 [47]
Wilhelm et al.,               LINE-1         Pyrosequencing
  2010 [35]
Zhang et al., 2011 [36]       LINE-1         Pyrosequencing
Zhang et al., 2012 [37]       LINE-1         Pyrosequencing
Zhu et al., 2012 [38]         LINE-1         Pyrosequencing
El-Maarri et al.,               Alu               SIRPH
  2007 [48]
Kim et al., 2010 [40]           Alu          Pyrosequencing
Rusiecki et al.,                Alu          Pyrosequencing
  2008 [42]
Zhu et al., 2012 [38]           Alu          Pyrosequencing
Fuke et al., 2004 [43]         5-mdC              HPLC

Race/Ethnicity

Hsiung et al., 2007 [28]      LINE-1            Cobra PCR
Perng et al., 2014 [34]       LINE-1         Pyrosequencing
Xu et al., 2012 [11]          LINE-1         Pyrosequencing
Zhang et al., 2011 [36]       LINE-1         Pyrosequencing
Zhang et al., 2012 [37]       LINE-1         Pyrosequencing
Choi et al., 2009 [1]          5-mdC          LC/ESI-MS/MS

Education

Agodi et al., 2015 [21]       LINE-1         Pyrosequencing
Hou et al., 2010 [27]         LINE-1         Pyrosequencing
Karami et al., 2015 [29]      LINE-1         Pyrosequencing
Perng et al., 2014 [34]       LINE-1         Pyrosequencing
Zhang et al., 2011 [36]       LINE-1         Pyrosequencing
Choi et al., 2009 [1]          5-mdC          LC/ESI-MS/MS

Authors                               Study participants

Age

Agodi et al., 2015 [21]         177 women aged 13-50, Helsinki
Bollati et al.,               718 individuals aged 55-92 from the
  2009 [22]                    Boston Area Normative Aging Study
Chalitchagorn                   32 individuals ranging in age,
  et al., 2004 [23]                        Thailand
Duggan et al., 2014 [24]      300 overweight women aged 50-75 in
                                            the US
El-Maarri et al.,              500 individuals aged 18-64, Bonn,
  2011 [25]                                 Germany
Gomes et al., 2012 [26]       126 individuals aged 60-88, Brazil
Hou et al., 2010 [27]            421 individuals aged 21-79 in
                                        Warsaw, Poland
Hsiung et al., 2007 [28]      765 individuals aged 18-75, Greater
                                   Boston Metropolitan Area
Karami et al., 2015 [29]      PLCO--436 controls from individuals
                                aged 55-74 in the US, ATBC--575
                             controls from individuals aged 55-69
                                          in Finland
Liao et al., 2011 [30]        654 individuals aged 20-79 from the
                              Central and Eastern European Renal
                                     Cancer Study (CEERCC)
Marques-Rocha et              156 individuals aged 19-27, Brazil
  al., 2016 [31]
Mirabello et al.,                 314 individuals aged 12-75+
  2010 [32]                     from the NCI Clinical Genetics
                             Branch Familial TGTC Study in the US
Pearce et al., 2012 [33]        228 individuals aged 49-51 from
                                      Newcastle, England
Perng et al., 2014 [34]           987 adults aged 45-84 from
                                   the Multi-Ethnic Study of
                                Atherosclerosis (MESA), NY & LA
Wilhelm et al.,               465 individuals aged 25-74, from NH
  2010 [35]
Xu et al., 2012 [11]                1101 women aged 20-98,
                              from The Long Island Breast Cancer
                                         Study Project
Zhang et al., 2011 [36]           161 individuals aged 45-75
                                 from the North Texas Healthy
                                          Heart Study
Zhang et al., 2012 [37]           165 individuals aged 18-78
                              from the COMIR (Commuting Mode and
                               Inflammatory Response) study, NY
Zhu et al., 2012 [38]             1465 individuals total from
                                      a combination of 5
                                 individual studies across MA;
                                 Warsaw, Poland; Milan, Italy;
                                Brescia, Italy; Trissino, Italy
Bollati et al., 2009 [22]         718 individuals aged 55-92
                                from the Boston Area Normative
                                          Aging Study
Fraga et al., 2005 [39]         80 monozygotic twins aged 3-74,
                                             Spain
Kim et al., 2010 [40]       86 individuals aged 42-69, South Korea
Na et al., 2014 [41]              244 women aged 20-51, Korea
Rusiecki et al.,                70 individuals aged 19-67 from
  2008 [42]                      Greenlandic Inuit, Greenland
Zhu et al., 2012 [38]            1465 individuals total from a
                              combination of 5 individual studies
                               across MA; Warsaw, Poland; Milan,
                            Italy; Brescia, Italy; Trissino, Italy
Choi et al., 2009 [1]                180 women aged 35-75
Fraga et al., 2005 [39]      80 monozygotic twins aged 3-74, Spain
Fuke et al., 2004 [43]             76 individuals aged 4-94
Moore et al., 2008 [44]         397 individuals aged 20-81 from
                               the Spanish Bladder Cancer Study,
                                             Spain

Sex

Andreotti et al.,             676 individuals aged 55-74 from the
  2014 [45]                   Prostate, Lung, Colorectal, Ovarian
                             Cancer Screening Trial (PLCO) in the
                                              US
Cash et al., 2012 [46]        528 individuals aged 25-74 from the
                              Residents Registry of the Shanghai
                                  Municipal Government, China
Chalitchagorn et                32 individuals ranging in age,
  al., 2004 [23]                           Thailand
El-Maarri et al.,              500 individuals aged 18-64, Bonn,
  2011 [25]                                 Germany
Hou et al., 2010 [27]        421 individuals aged 21-79 in Warsaw,
                                            Poland
Hsiung et al., 2007 [28]      765 individuals aged 18-75, Greater
                                   Boston Metropolitan Area
Karami et al., 2015 [29]      PLCO, 436 controls from individuals
                                     aged 55-74 in the US
Liao et al., 2011 [30]            654 individuals aged 20-79
                             from the Central and Eastern European
                                  Renal Cancer Study (CEERCC)
Mirabello et al.,            314 individuals aged 12-75+ from the
  2010 [32]                  NCI Clinical Genetics Branch Familial
                                     TGTC Study in the US
Pearce et al., 2012 [33]        228 individuals aged 49-51 from
                                      Newcastle, England
Perng et al., 2014 [34]         987 adults aged 45-84 from the
                             Multi-Ethnic Study of Atherosclerosis
                                        (MESA), NY & LA
Rusiecki et al.,                70 individuals aged 19-67 from
  2008 [42]                      Greenlandic Inuit, Greenland
Tajuddin et al.,              892 individuals aged 20-81 from the
  2013 [47]                  Spanish Bladder Cancer/EPICURO study,
                                             Spain
Wilhelm et al.,               465 individuals aged 25-74, from NH
  2010 [35]
Zhang et al., 2011 [36]       161 individuals aged 45-75 from the
                                North Texas Healthy Heart Study
Zhang et al., 2012 [37]           165 individuals aged 18-78
                              from the COMIR (Commuting Mode and
                               Inflammatory Response) study, NY
Zhu et al., 2012 [38]             1465 individuals total from
                                      a combination of 5
                                 individual studies across MA;
                                 Warsaw, Poland; Milan, Italy;
                                Brescia, Italy; Trissino, Italy
El-Maarri et al.,              192 individuals aged 18-43, Bonn,
  2007 [48]                                 Germany
Kim et al., 2010 [40]          86 individuals aged 42-69, South
                                             Korea
Rusiecki et al.,                70 individuals aged 19-67 from
  2008 [42]                      Greenlandic Inuit, Greenland
Zhu et al., 2012 [38]             1465 individuals total from
                             a combination of 5 individual studies
                               across MA; Warsaw, Poland; Milan,
                               Italy; Brescia, Italy; Trissino,
                                             Italy
Fuke et al., 2004 [43]             76 individuals aged 4-94

Race/Ethnicity

Hsiung et al., 2007 [28]      765 individuals aged 18-75, Greater
                                   Boston Metropolitan Area
Perng et al., 2014 [34]      987 adults aged 45-84 from the Multi
                               -Ethnic Study of Atherosclerosis
                                        (MESA), NY & LA
Xu et al., 2012 [11]                1101 women aged 20-98,
                              from The Long Island Breast Cancer
                                         Study Project
Zhang et al., 2011 [36]       161 individuals aged 45-75 from the
                                North Texas Healthy Heart Study

Zhang et al., 2012 [37]           165 individuals aged 18-78
                              from the COMIR (Commuting Mode and
                               Inflammatory Response) study, NY
Choi et al., 2009 [1]                180 women aged 35-75

Education

Agodi et al., 2015 [21]         177 women aged 13-50, Helsinki
Hou et al., 2010 [27]        421 individuals aged 21-79 in Warsaw,
                                            Poland
Karami et al., 2015 [29]            PLCO--436 controls from
                               individuals aged 55-74 in the US,
                              ATBC--575 controls from individuals
                                     aged 55-69 in Finland
Perng et al., 2014 [34]           987 adults aged 45-84 from
                                   the Multi-Ethnic Study of
                                Atherosclerosis (MESA), NY & LA
Zhang et al., 2011 [36]           161 adults aged 45-75 from
                              the North Texas Healthy Heart Study
Choi et al., 2009 [1]                180 women aged 35-75

Authors                         Findings             Comments

Age

Agodi et al., 2015 [21]      No differences
Bollati et al.,              No differences
  2009 [22]
Chalitchagorn                No differences
  et al., 2004 [23]
Duggan et al., 2014 [24]     No differences
El-Maarri et al.,            No differences
  2011 [25]
Gomes et al., 2012 [26]      No differences
Hou et al., 2010 [27]        No differences     Data was stratified
                                                 by gender. Before
                                                  stratification,
                                                 association with
                                                age was significant
Hsiung et al., 2007 [28]     No differences      Adjusted for sex,
                                                  race, smoking,
                                                   alcohol, HPV
                                                 serology, dietary
                                                   folate, MTHFR
Karami et al., 2015 [29]        PLCO: No          ATBC, increased
                            differences ATBC:     age associated
                               significant          with higher
                               difference           methylation
                               between age          levels. Age
                               groups (p <        53-54 has 78.34
                                 0.001)               LINE-1
                                                   methylation%,
                                                  55-59 has 78.42
                                                      LINE-1
                                                   methylation%,
                                                  60-64 has 78.68
                                                      LINE-1
                                                   methylation%,
                                                  65-69 has 79.34
                                                      LINE-1
                                                   methylation%,
                                                  70-76 has 79.60
                                                      LINE-1
                                                   methylation%
Liao et al., 2011 [30]       No differences
Marques-Rocha et             No differences
  al., 2016 [31]
Mirabello et al.,            No differences      Adjusted for sex
  2010 [32]
Pearce et al., 2012 [33]     No differences
Perng et al., 2014 [34]      No differences
Wilhelm et al.,              No differences
  2010 [35]
Xu et al., 2012 [11]         No differences
Zhang et al., 2011 [36]      No differences
Zhang et al., 2012 [37]      No differences
Zhu et al., 2012 [38]        No differences
Bollati et al., 2009 [22]      Significant         Increased age
                               differences        associated with
                               (p = 0.012)        an average 0.2
                               between age       5-mdC percentage
                                 groups              decrease
Fraga et al., 2005 [39]        Significant       Youngest pairs of
                               differences           MZ twins
                               (p < 0.05)         epigenetically
                               between age       similar, whereas
                                 groups            oldest pairs
                                                 clearly distinct
Kim et al., 2010 [40]          Significant         Statistically
                               differences          significant
                               (p < 0.35)             inverse
                               between age       association with
                                 groups          DNA methylation.
                                                 Adjusted for age
Na et al., 2014 [41]         No differences
Rusiecki et al.,             No differences
  2008 [42]
Zhu et al., 2012 [38]        No differences
Choi et al., 2009 [1]        No differences
Fraga et al., 2005 [39]        Significant       Youngest pairs of
                               differences           MZ twins
                               (p < 0.05)         epigenetically
                               between age       similar, whereas
                                 groups            oldest pairs
                                                 clearly distinct
Fuke et al., 2004 [43]         Significant         Increased age
                               differences        associated with
                              (p < 0.0002)           decreased
                               between age          methylation
                                 groups          levels. Age 4-14
                                                  has 4.018% metC
                                                  /dC + metC, age
                                                 16-22 has 4.03%,
                                                   age 25-41 has
                                                  3.977%, and age
                                                 51-94 has 3.948%
Moore et al., 2008 [44]      No differences

Sex

Andreotti et al.,              Significant        Males had 84.2%
  2014 [45]                    differences        average LINE-1
                              (p < 0.0004)         methylation%,
                              between male       Females had 83.5%
                               and female         average LINE-1
                                                   methylation%
Cash et al., 2012 [46]         Significant        Males had 82.09
                               differences        average LINE-1
                              (p < 0.0004)         methylation%,
                              between male          Females had
                               and female         81.53% average
                                                      LINE-1
                                                   methylation%
Chalitchagorn et             No differences
  al., 2004 [23]
El-Maarri et al.,              Significant        Average gender
  2011 [25]                    differences       difference 0.94%
                               (p < 0.01)
                              between male
                               and female
Hou et al., 2010 [27]        No differences
Hsiung et al., 2007 [28]       Significant          Not given;
                               differences         adjusted for
                               (p < 0.002)          age, race,
                             between "male"          smoking,
                              and "female"         alcohol, HPV
                                                     serology,
                                                      dietary
                                                   folate, MTHFR
Karami et al., 2015 [29]          PLCO,          Males had 77.15%
                               Significant        average LINE-1
                               differences         methylation%,
                              (p < 0.0001)          females had
                              between male        76.58% average
                               and female             LINE-1
                                                   methylation%
Liao et al., 2011 [30]         Significant       Males had 81.97%
                               differences        average LINE-1
                              (p < 0.0003)         methylation%,
                              between male       females had 81.4%
                               and female         average LINE-1
                                                   methylation%
Mirabello et al.,              Significant        Males had 79.6%
  2010 [32]                    differences        average LINE-1
                               (p < 0.002)         methylation%,
                              between male          females had
                               and female         78.87% average
                                                      LINE-1
                                                   methylation%.
                                                 Adjusted for age
Pearce et al., 2012 [33]     No differences
Perng et al., 2014 [34]        Significant       Males had 80.94%
                               differences        average LINE-1
                              (p < 0.0001)         methylation%,
                              between male          Females had
                               and female         80.54% average
                                                      LINE-1
                                                   methylation%
Rusiecki et al.,               Significant       Males had 79.05%
  2008 [42]                    differences        average LINE-1
                               (p < 0.002)         methylation%,
                              between male          Females had
                               and female         77.73% average
                                                      LINE-1
                                                   methylation%
Tajuddin et al.,             No differences         Significant
  2013 [47]                                         differences
                                                    (p = 0.02)
                                                   between male
                                                    and female
                                                      before
                                                    Bonferroni
                                                    correction
Wilhelm et al.,                Significant           Not given
  2010 [35]                    differences
                               (p < 0.04)
                              between male
                               and female
Zhang et al., 2011 [36]        Significant         Males had 75%
                               differences        average LINE-1
                              (p < 0.0001)         methylation%,
                              between male       females had 73.2%
                               and female         average LINE-1
                                                   methylation%
Zhang et al., 2012 [37]      No differences
Zhu et al., 2012 [38]        No differences
El-Maarri et al.,              Significant        Slightly higher
  2007 [48]                    differences        methylation in
                              (p < 0.0003)             males
                              between male
                               and female
Kim et al., 2010 [40]        No differences      Adjusted for age
Rusiecki et al.,               Significant       Males had 25.35%
  2008 [42]                    differences          average Alu
                              (p < 0.0001)         methylation%,
                              between male      Females had 24.69%
                               and female           average Alu
                                                   methylation%
Zhu et al., 2012 [38]        No differences
Fuke et al., 2004 [43]         Significant        Males had metC/
                               differences      (dC + metC) = 4.01
                              (p < 0.0067)        [+ or -] 0.069,
                              between male       females had metC
                               and female          /(dC + metC)
                                                 = 3.975 [+ or -]
                                                       0.067

Race/Ethnicity

Hsiung et al., 2007 [28]       Significant         Not provided;
                               differences       Adjusted for age,
                               (p = 0.03)          sex, smoking,
                              between "non         alcohol, HPV
                             -Caucasian" and     serology, dietary
                               "Caucasian"         folate, MTHFR
Perng et al., 2014 [34]        Significant       Caucasian Whites
                               differences       had 80.5% average
                               (p = 0.008)            LINE-1
                              found between        methylation%,
                               "Caucasian        African-American
                                Whites",         Blacks had 80.84%
                                "African          average LINE-1
                                -American          methylation%,
                              Blacks", and         Hispanics had
                                Hispanics         80.75% average
                                                      LINE-1
                                                   methylation%
Xu et al., 2012 [11]         No differences
Zhang et al., 2011 [36]        Significant         Non-Hispanic
                             differences (p      Whites had 75.3%
                             = 0.001) found       average LINE-1
                              between "non         methylation%,
                                -Hispanic          non-Hispanic
                              Whites", "non      Blacks had 73.1%
                                -Hispanic         average LINE-1
                              Blacks", and         methylation%,
                                Hispanics          Hispanics had
                                                    74% average
                                                      LINE-1
                                                   methylation%
Zhang et al., 2012 [37]      No differences
Choi et al., 2009 [1]        No differences

Education

Agodi et al., 2015 [21]      No differences
Hou et al., 2010 [27]        No differences
Karami et al., 2015 [29]         PLCO-No
                               differences
                                 ATBC-No
                               differences
Perng et al., 2014 [34]      No differences
Zhang et al., 2011 [36]      No differences
Choi et al., 2009 [1]        No differences

TABLE 3: Study findings for lifestyle, dietary, and
reproductive factors.

Authors             Methylation   Measurement method
                       Type

Physical activity

Duggan et al.,        LINE-1        Pyrosequencing
  2014 [24]
Perng et al.,         LINE-1        Pyrosequencing
  2014 [34]
White et al.,         LINE-1        Pyrosequencing
  2013 [49]
Zhang et al.,         LINE-1          MethyLight
  2011 [50]
Zhang et al.,         LINE-1        Pyrosequencing
  2012 [37]

Alcohol

Agodi et al.,         LINE-1        Pyrosequencing
  2015 [21]
Hou et al.,           LINE-1        Pyrosequencing
  2010 [27]
Hsiung et al.,        LINE-1           COBRA PCR
  2007 [28]
Karami et al.,        LINE-1        Pyrosequencing
  2015 [29]
Mirabello et al.,     LINE-1        Pyrosequencing
  2010 [32]
Pearce et al.,        LINE-1        Pyrosequencing
  2012 [33]
Tajuddin et al.,      LINE-1        Pyrosequencing
  2013 [47]
Perng et al.,         LINE-1        Pyrosequencing
  2014 [34]
Xu et al.,            LINE-1        Pyrosequencing
  2012 [11]
Zhang et al.,         LINE-1        Pyrosequencing
  2011 [36]
Zhang et al.,         LINE-1        Pyrosequencing
  2012 [37]
Zhu et al.,           LINE-1        Pyrosequencing
  2012 [38]
Zhu et al.,             Au          Pyrosequencing
  2012 [38]
Kim et al.,             Alu         Pyrosequencing
  2010 [40]
Na et al.,              Alu         Pyrosequencing
  2014 [41]
Choi et al.,           5-mdC         LC/ESI-MS/MS
  2009 [1]

Smoking

Agodi et al.,         LINE-1        Pyrosequencing
  2015 [21]
Andreotti et          LINE-1        Pyrosequencing
  al., 2014 [45]
Gomes et al.,         LINE-1             ELISA
  2012 [26]
Hou et al.,           LINE-1        Pyrosequencing
  2010 [27]
Hsiung et al.,        LINE-1           COBRA PCR
  2007 [28]
Karami et al.,        LINE-1        Pyrosequencing
  2015 [29]
Liao et al.,          LINE-1        Pyrosequencing
  2011 [30]
Mirabello et          LINE-1        Pyrosequencing
  al., 2010 [32]
Pearce et al.,        LINE-1        Pyrosequencing
  2012 [33]
Perng et al.,         LINE-1        Pyrosequencing
  2014 [34]
Tajuddin et           LINE-1        Pyrosequencing
  al., 2013 [47]
Wilhelm et al.,       LINE-1        Pyrosequencing
  2010 [35]
Xu et al.,            LINE-1        Pyrosequencing
  2012 [11]
Zhang et al.,         LINE-1        Pyrosequencing
  2011 [36]
Zhu et al.,           LINE-1        Pyrosequencing
  2012 [38]
Kim et al.,             Alu         Pyrosequencing
  2010 [40]
Na et al.,              Alu         Pyrosequencing
  2014 [41]
Rusiecki et al.,        Alu         Pyrosequencing
  2008 [42]
Zhu et al.,             Alu         Pyrosequencing
  2012 [38]
Choi et al.,           5-mdC         LC/ESI-MS/MS
  2009 [1]
Moore et al.,          5-mdC      HPCE, Hpall digest,
  2008 [44]                          densitometry

BMI

Agodi et al.,         LINE-1        Pyrosequencing
  2015 [21]
Duggan et al.,        LINE-1        Pyrosequencing
  2014 [24]
Gomes et al.,         LINE-1             ELISA
  2012 [26]
Karami et al.,        LINE-1        Pyrosequencing
  2015 [29]
Liao et al.,          LINE-1        Pyrosequencing
  2011 [30]
Marques-Rocha,        LINE-1            MS-HRM
  2016 [31]
Pearce et al.,        LINE-1        Pyrosequencing
  2012 [33]
Perng et al.,         LINE-1        Pyrosequencing
  2014 [34]
Tajuddin et           LINE-1        Pyrosequencing
  al., 2013 [47]
Zhang et al.,         LINE-1        Pyrosequencing
  2011 [36]
Zhang et al.,         LINE-1        Pyrosequencing
  2012 [37]
Zhu et al.,           LINE-1        Pyrosequencing
  2012 [38]
Kim et al.,             Alu         Pyrosequencing
  2010 [40]
Na et al.,              Alu         Pyrosequencing
  2014 [41]
Zhu et al.,             Alu         Pyrosequencing
  2012 [38]
Choi et al.,           5-mdC         LC/ESI-MS/MS
  2009 [1]

Vegetables

Agodi et al.,         LINE-1        Pyrosequencing
  2015 [21]
Cash et al.,          LINE-1        Pyrosequencing
  2012 [46]
Duggan et al.,        LINE-1        Pyrosequencing
  2014 [24]
Hou et al.,           LINE-1        Pyrosequencing
  2010 [27]
Karami et al.,        LINE-1        Pyrosequencing
  2015 [29]
Liao et al.,          LINE-1        Pyrosequencing
  2011 [30]
Martin-Nunez et       LINE-1        Pyrosequencing
  al., 2014 [51]
Tajuddin et al.,      LINE-1        Pyrosequencing
  2013 [47]
Zhang et al.,         LINE-1        Pyrosequencing
  2012 [37]

Fruit

Agodi et al.,         LINE-1        Pyrosequencing
  2015 [21]
Hou et al.,           LINE-1        Pyrosequencing
  2010 [27]
Karami et al.,        LINE-1        Pyrosequencing
  2015 [29]
Tajuddin et al.,      LINE-1        Pyrosequencing
  2013 [47]
Zhang et al.,         LINE-1        Pyrosequencing
  2012 [37]

Folate

Agodi et al.,         LINE-1        Pyrosequencing
  2015 [21]
Bae et al.,           LINE-1           LC-MS/MS
  2014 [52]
Gomes et al.,         LINE-1             ELISA
  2012 [26]
Hou et al.,           LINE-1        Pyrosequencing
  2010 [27]
Hsiung et al.,        LINE-1           COBRA PCR
  2007 [28]
Karami et al.,        LINE-1        Pyrosequencing
  2015 [29]
Perng et al.,         LINE-1        Pyrosequencing
  2014 [34]
Tajuddin et           LINE-1        Pyrosequencing
  al., 2013 [47]
Xu et al.,            LINE-1        Pyrosequencing
  2012 [11]
Zhang et al.,         LINE-1        Pyrosequencing
  2011 [36]
Zhang et al.,         LINE-1        Pyrosequencing
  2012 [37]
Moore et al.,          5-mdC      HPCE, Hpall digest,
  2008 [44]                          densitometry

Menopause status

Xu et al.,            LINE-1        Pyrosequencing
  2012 [11]
Choi et al.,           5-mdC         LC/ESI-MS/MS
  2009 [1]

Fetal Birthweight

Michels et al.,       LINE-1        Pyrosequencing
  2011 [53]

Family history
of breast cancer

Brennan et al.,       LINE-1        Pyrosequencing
  2012 [9]
Delgado-Cruzata       LINE-1          MethyLight
  et al.,
  2014 [54]

Wu et al.,            LINE-1        Pyrosequencing,
  2011 [55]                           MethyLight
Xu et al.,            LINE-1        Pyrosequencing
  2012 [11]

Wu et al.,              Alu           MethyLight
  2011 [55]

Choi et al.,           5-mdC         LC/ESI-MS/MS
  2009 [1]

Age at Menarche

Choi et al.,           5-mdC         LC/ESI-MS/MS
  2009 [1]

Age at first
birth

Choi et al.,           5-mdC         LC/ESI-MS/MS
  2009 [1]

Parity

Choi et al.,           5-mdC         LC/ESI-MS/MS
  2009 [1]

Hormone Cycle

El-Maarri et          LINE-1        Pyrosequencing,
  al., 2011 [25]                         SIRPH

Sex Hormones

Iwasaki et al.,       LINE-1             LUMA
  2012 [56]
Ulrich et al.,        LINE-1        Pyrosequencing
  2012 [57]

Hormone use

Choi et al.,           5-mdC         LC/ESI-MS/MS
  2009 [1]

Authors                 Study participants

Physical activity

Duggan et al.,       300 overweight women aged
  2014 [24]               50-75 in the US
Perng et al.,       987 adults aged 45-84 from
  2014 [34]          the Multi-Ethnic Study of
                      Atherosclerosis (MESA),
                              NY & LA
White et al.,         647 non-Hispanic white
  2013 [49]          women aged 35-74 from the
                       NIH sister study, USA
Zhang et al.,       161 individuals aged 45-75
  2011 [50]            from the North Texas
                        Healthy Heart Study
Zhang et al.,       165 individuals aged 18-78
  2012 [37]          from the COMIR (Commuting
                       Mode and Inflammatory
                        Response) study, NY

Alcohol

Agodi et al.,          177 women aged 13-50,
  2015 [21]                  Helsinki
Hou et al.,         421 individuals aged 21-79
  2010 [27]              in Warsaw, Poland
Hsiung et al.,         765 individuals aged
  2007 [28]            18-75, Greater Boston
                         Metropolitan Area
Karami et al.,        PLCO--436 controls from
  2015 [29]          individuals aged 55-74 in
                    the US, ATBC, 575 controls
                       from individuals aged
                         55-69 in Finland
Mirabello et al.,   314 individuals aged 12-75+
  2010 [32]            from the NCI Clinical
                     Genetics Branch Familial
                       TGTC Study in the US
Pearce et al.,      228 individuals aged 49-51
  2012 [33]           from Newcastle, England
Tajuddin et al.,    892 individuals aged 20-81
  2013 [47]          from the Spanish Bladder
                    Cancer/EPICURO study, Spain
Perng et al.,       987 adults aged 45-84 from
  2014 [34]          the Multi-Ethnic Study of
                      Atherosclerosis (MESA),
                              NY & LA
Xu et al.,            1101 women aged 20-98,
  2012 [11]         from The Long Island Breast
                       Cancer Study Project
Zhang et al.,       161 individuals aged 45-75
  2011 [36]            from the North Texas
                        Healthy Heart Study
Zhang et al.,       165 individuals aged 18-78
  2012 [37]          from the COMIR (Commuting
                       Mode and Inflammatory
                        Response) study, NY
Zhu et al.,           1465 individuals total
  2012 [38]           from a combination of 5
                     individual studies across
                    MA; Warsaw, Poland; Milan,
                      Italy; Brescia, Italy;
                          Trissino, Italy
Zhu et al.,         1465 individuals total from
  2012 [38]             a combination of 5
                     individual studies across
                    MA; Warsaw, Poland; Milan,
                      Italy; Brescia, Italy;
                          Trissino, Italy
Kim et al.,         86 individuals aged 42-69,
  2010 [40]                 South Korea
Na et al.,          244 women aged 20-51, Korea
  2014 [41]
Choi et al.,           180 women aged 35-75
  2009 [1]

Smoking

Agodi et al.,          177 women aged 13-50,
  2015 [21]                  Helsinki
Andreotti et        676 individuals aged 55-74
  al., 2014 [45]     from the Prostate, Lung,
                    Colorectal, Ovarian Cancer
                     Screening Trial (PLCO) in
                              the US
Gomes et al.,       126 individuals aged 60-88,
  2012 [26]                   Brazil
Hou et al.,         421 individuals aged 21-79
  2010 [27]              in Warsaw, Poland
Hsiung et al.,      765 individuals aged 18-75,
  2007 [28]               Greater Boston
                         Metropolitan Area
Karami et al.,        PLCO--436 controls from
  2015 [29]          individuals aged 55-74 in
                              the US
Liao et al.,        654 individuals aged 20-79
  2011 [30]            from the Central and
                      Eastern European Renal
                       Cancer Study (CEERCC)
Mirabello et        314 individuals aged 12-75+
  al., 2010 [32]       from the NCI Clinical
                     Genetics Branch Familial
                       TGTC Study in the US
Pearce et al.,      228 individuals aged 49-51
  2012 [33]           from Newcastle, England
Perng et al.,       987 adults aged 45-84 from
  2014 [34]          the Multi-Ethnic Study of
                    Atherosclerosis (MESA), NY
                               & LA
Tajuddin et         892 individuals aged 20-81
  al., 2013 [47]     from the Spanish Bladder
                    Cancer/EPICURO study, Spain
Wilhelm et al.,     465 individuals aged 25-74,
  2010 [35]                   from NH
Xu et al.,          1101 women aged 20-98, from
  2012 [11]           The Long Island Breast
                       Cancer Study Project
Zhang et al.,       161 individuals aged 45-75
  2011 [36]            from the North Texas
                        Healthy Heart Study
Zhu et al.,         1465 individuals total from
  2012 [38]             a combination of 5
                     individual studies across
                    MA; Warsaw, Poland; Milan,
                      Italy; Brescia, Italy;
                          Trissino, Italy
Kim et al.,         86 individuals aged 42-69,
  2010 [40]                 South Korea
Na et al.,          244 women aged 20-51, Korea
  2014 [41]
Rusiecki et al.,     70 individuals aged 19-67
  2008 [42]           from Greenlandic Inuit,
                             Greenland
Zhu et al.,         1465 individuals total from
  2012 [38]             a combination of 5
                     individual studies across
                    MA; Warsaw, Poland; Milan,
                      Italy; Brescia, Italy;
                          Trissino, Italy
Choi et al.,           180 women aged 35-75
  2009 [1]
Moore et al.,       397 individuals aged 20-81
  2008 [44]          from the Spanish Bladder
                        Cancer Study, Spain

BMI

Agodi et al.,          177 women aged 13-50,
  2015 [21]                  Helsinki
Duggan et al.,       300 overweight women aged
  2014 [24]               50-75 in the US
Gomes et al.,       126 individuals aged 60-88,
  2012 [26]                   Brazil
Karami et al.,        PLCO, 436 controls from
  2015 [29]          individuals aged 55-74 in
                    the US, ATBC, 575 controls
                    from individuals aged 55-69
                            in Finland
Liao et al.,        654 individuals aged 20-79
  2011 [30]            from the Central and
                      Eastern European Renal
                       Cancer Study (CEERCC)
Marques-Rocha,      156 individuals aged 19-27,
  2016 [31]                   Brazil
Pearce et al.,      228 individuals aged 49-51
  2012 [33]           from Newcastle, England
Perng et al.,       987 adults aged 45-84 from
  2014 [34]          the Multi-Ethnic Study of
                    Atherosclerosis (MESA), NY
                               & LA
Tajuddin et         892 individuals aged 20-81
  al., 2013 [47]     from the Spanish Bladder
                    Cancer/EPICURO study, Spain
Zhang et al.,       161 individuals aged 45-75
  2011 [36]            from the North Texas
                        Healthy Heart Study
Zhang et al.,       165 individuals aged 18-78
  2012 [37]          from the COMIR (Commuting
                       Mode and Inflammatory
                        Response) study, NY
Zhu et al.,         1465 individuals total from
  2012 [38]             a combination of 5
                     individual studies across
                    MA; Warsaw, Poland; Milan,
                      Italy; Brescia, Italy;
                          Trissino, Italy
Kim et al.,         86 individuals aged 42-69,
  2010 [40]                 South Korea
Na et al.,          244 women aged 20-51, Korea
  2014 [41]
Zhu et al.,         1465 individuals total from
  2012 [38]             a combination of 5
                     individual studies across
                    MA; Warsaw, Poland; Milan,
                      Italy; Brescia, Italy;
                          Trissino, Italy
Choi et al.,           180 women aged 35-75
  2009 [1]

Vegetables

Agodi et al.,          177 women aged 13-50,
  2015 [21]                  Helsinki
Cash et al.,           528 individuals aged
  2012 [46]          25-74 from the Residents
                     Registry of the Shanghai
                    Municipal Government, China
Duggan et al.,       300 overweight women aged
  2014 [24]               50-75 in the US
Hou et al.,         421 individuals aged 21-79
  2010 [27]              in Warsaw, Poland
Karami et al.,        PLCO--436 controls from
  2015 [29]          individuals aged 55-74 in
                    the US, ATBC, 575 controls
                    from individuals aged 55-69
                            in Finland
Liao et al.,        654 individuals aged 20-79
  2011 [30]            from the Central and
                      Eastern European Renal
                       Cancer Study (CEERCC)
Martin-Nunez et     155 individuals aged 40-65
  al., 2014 [51]            from Spain
Tajuddin et al.,    892 individuals aged 20-81
  2013 [47]          from the Spanish Bladder
                    Cancer/EPICURO study, Spain
Zhang et al.,       165 individuals aged 18-78
  2012 [37]          from the COMIR (Commuting
                       Mode and Inflammatory
                        Response) study, NY

Fruit

Agodi et al.,          177 women aged 13-50,
  2015 [21]                  Helsinki
Hou et al.,         421 individuals aged 21-79
  2010 [27]              in Warsaw, Poland
Karami et al.,        PLCO, 436 controls from
  2015 [29]          individuals aged 55-74 in
                    the US, ATBC, 575 controls
                       from individuals aged
                         55-69 in Finland
Tajuddin et al.,    892 individuals aged 20-81
  2013 [47]          from the Spanish Bladder
                    Cancer/EPICURO study, Spain
Zhang et al.,       165 individuals aged 18-78
  2012 [37]          from the COMIR (Commuting
                       Mode and Inflammatory
                        Response) study, NY

Folate

Agodi et al.,          177 women aged 13-50,
  2015 [21]                  Helsinki
Bae et al.,            408 women aged 50-79
  2014 [52]           from the WHI-OS cohort,
                         throughout the US
Gomes et al.,       126 individuals aged 60-88,
  2012 [26]                   Brazil
Hou et al.,         421 individuals aged 21-79
  2010 [27]              in Warsaw, Poland
Hsiung et al.,         765 individuals aged
  2007 [28]            18-75, Greater Boston
                         Metropolitan Area
Karami et al.,        PLCO, 436 controls from
  2015 [29]          individuals aged 55-74 in
                    the US, ATBC, 575 controls
                    from individuals aged 55-69
                            in Finland
Perng et al.,       987 adults aged 45-84 from
  2014 [34]          the Multi-Ethnic Study of
                    Atherosclerosis (MESA), NY
                               & LA
Tajuddin et         892 individuals aged 20-81
  al., 2013 [47]     from the Spanish Bladder
                    Cancer/EPICURO study, Spain
Xu et al.,          1101 women aged 20-98, from
  2012 [11]           The Long Island Breast
                       Cancer Study Project
Zhang et al.,       161 individuals aged 45-75
  2011 [36]            from the North Texas
                        Healthy Heart Study
Zhang et al.,       165 individuals aged 18-78
  2012 [37]          from the COMIR (Commuting
                       Mode and Inflammatory
                        Response) study, NY
Moore et al.,       397 individuals aged 20-81
  2008 [44]          from the Spanish Bladder
                        Cancer Study, Spain

Menopause status

Xu et al.,            1101 women aged 20-98,
  2012 [11]         from The Long Island Breast
                       Cancer Study Project
Choi et al.,           180 women aged 35-75
  2009 [1]

Fetal Birthweight

Michels et al.,       319 mother-child dyads
  2011 [53]          from Brigham and Women's
                         Hospital, Boston

Family history
of breast cancer

Brennan et al.,     769 individuals aged 23-83
  2012 [9]              from 3 cohorts, USA
Delgado-Cruzata      333 unaffected women who
  et al.,            had a sister with breast
  2014 [54]           cancer from the Breast
                    Cancer Family Registry, NY
Wu et al.,            51 girls aged 6-17, USA
  2011 [55]
Xu et al.,            1101 women aged 20-98,
  2012 [11]         from The Long Island Breast
                       Cancer Study Project
Wu et al.,            51 girls aged 6-17, USA
  2011 [55]
Choi et al.,           180 women aged 35-75
  2009 [1]

Age at Menarche

Choi et al.,           180 women aged 35-75
  2009 [1]

Age at first
birth

Choi et al.,           180 women aged 35-75
  2009 [1]

Parity

Choi et al.,           180 women aged 35-75
  2009 [1]

Hormone Cycle

El-Maarri et           500 individuals aged
  al., 2011 [25]       18-64, Bonn, Germany

Sex Hormones

Iwasaki et al.,     185 women aged 55-74, Japan
  2012 [56]
Ulrich et al.,       173 women aged 55-75 from
  2012 [57]          the Physical Activity for
                        Total Health Study

Hormone use

Choi et al.,           180 women aged 35-75
  2009 [1]

Authors                    Findings                 Comments

Physical activity

Duggan et al.,          No differences
  2014 [24]
Perng et al.,           No differences
  2014 [34]
White et al.,            Significant            Physical activity
  2013 [49]         differences (p = 0.04)       levels of women
                     between "0" and "3"         greater than or
                      physical activity        equal to the median
                     duration level above     of physical activity
                    the median of physical    at three time points
                           activity             (ages 5-12, 13-19
                                               and currently) had
                                                  higher global
                                              methylation compared
                                                  to women with
                                                 activity levels
                                                below the median
                                               for all three time
                                              periods (beta = .33,
                                               95% CI: .01, 0.66)
Zhang et al.,           No differences
  2011 [50]
Zhang et al.,
  2012 [37]             No differences

Alcohol

Agodi et al.,           No differences
  2015 [21]
Hou et al.,             No differences
  2010 [27]
Hsiung et al.,          No differences          Adjusted for age,
  2007 [28]                                    sex, race, smoking,
                                                  HPV serology,
                                              dietary folate, MTHFR
Karami et al.,       PLCO--No differences
  2015 [29]          ATBC--No differences
Mirabello et al.,       No differences
  2010 [32]
Pearce et al.,          No differences
  2012 [33]
Tajuddin et al.,        No differences
  2013 [47]
Perng et al.,           No differences
  2014 [34]
Xu et al.,              No differences
  2012 [11]
Zhang et al.,           No differences
  2011 [36]
Zhang et al.,           No differences
  2012 [37]
Zhu et al.,             No differences
  2012 [38]
Zhu et al.,             No differences
  2012 [38]
Kim et al.,             No differences          Adjusted for age
  2010 [40]
Na et al.,              No differences
  2014 [41]
Choi et al.,            No differences
  2009 [1]

Smoking

Agodi et al.,           No differences
  2015 [21]           No difference for         Never smoked had
Andreotti et         females. Significant      84% average LINE-1
  al., 2014 [45]       differences (p =         methylation% and
                    0.008) between "Never"       Ever smoked had
                          and "Ever"              83.6% average
                      smokers for males        LINE-1 methylation%
                                                    for males
Gomes et al.,           No differences
  2012 [26]
Hou et al.,             No differences
  2010 [27]
Hsiung et al.,          No differences          Adjusted for age,
  2007 [28]                                    sex, race, alcohol,
                                              HPV serology, dietary
                                                  folate, MTHFR
Karami et al.,       PLCO--No differences      PLCO, males who had
  2015 [29]              for females.         never smoked have an
                    Significant difference    average 77.35% LINE-1
                      (p = 0.02) between        methylation%, and
                    smokers and nonsmokers     males who had ever
                          for males          smoked have an average
                                                  77.02% LINE-1
                                                  methylation%
Liao et al.,            No differences
  2011 [30]
Mirabello et            No differences
  al., 2010 [32]
Pearce et al.,          No differences
  2012 [33]
Perng et al.,           No differences
  2014 [34]
Tajuddin et             No differences          Adjusted for age,
  al., 2013 [47]                                   sex, region
Wilhelm et al.,         No differences
  2010 [35]
Xu et al.,              No differences
  2012 [11]
Zhang et al.,           No differences
  2011 [36]
Zhu et al.,             No differences
  2012 [38]
Kim et al.,             No differences          Adjusted for age
  2010 [40]
Na et al.,              No differences
  2014 [41]
Rusiecki et al.,        No differences
  2008 [42]
Zhu et al.,             No differences
  2012 [38]
Choi et al.,            No differences
  2009 [1]
Moore et al.,           No differences
  2008 [44]

BMI

Agodi et al.,           No differences
  2015 [21]
Duggan et al.,          No differences
  2014 [24]
Gomes et al.,           No differences
  2012 [26]
Karami et al.,      PLCO, no differences.       BMI 16.7-<25 had
  2015 [29]           ATBC, significant          79.00% average
                     differences between      LINE-1 methylation%,
                     16.7-<25, 25-30, and         BMI 25-30 had
                           30-62.1               78.73% average
                                              LINE-1 methylation%,
                                                 and BMI 30-62.1
                                               had 78.39% average
                                               LINE-1 methylation%
Liao et al.,            No differences
  2011 [30]
Marques-Rocha,          No differences
  2016 [31]
Pearce et al.,          No differences          Adjusted for sex
  2012 [33]
Perng et al.,           No differences
  2014 [34]
Tajuddin et             No differences
  al., 2013 [47]
Zhang et al.,           No differences
  2011 [36]
Zhang et al.,           No differences        In unadjusted models,
  2012 [37]                                        there was a
                                                  statistically
                                                   significant
                                              difference (p = 0.03)
Zhu et al.,             No differences
  2012 [38]
Kim et al.,             No differences          Adjusted for age
  2010 [40]
Na et al.,               Significant            Normal weight had
  2014 [41]         difference (p < 0.001)   26.28 Alu methylation%,
                    between normal weight,    overweight had 24.95
                    overweight, and obese       Alu methylation%,
                            groups              normal weight had
                                             25.96 Alu methylation%
Zhu et al.,             No differences
  2012 [38]
Choi et al.,            No differences
  2009 [1]

Vegetables

Agodi et al.,           No differences
  2015 [21]
Cash et al.,             Significant           Men with "<4 times
  2012 [46]            differences (p =         /week" intake of
                      0.002) between "<4        total cruciferous
                     times/week" and ">4      vegetables had 81.31
                      times/week" intake         average LINE-1
                     of total cruciferous     methylation% and men
                      vegetables in men,      with ">4 times/week"
                      not significant in         intake of total
                            women                  cruciferous
                                               vegetables had 82.2
                                                 average LINE-1
                                                  methylation%
Duggan et al.,          No differences
  2014 [24]
Hou et al.,             No differences
  2010 [27]
Karami et al.,             PLCO, No              <690.9 grams of
  2015 [29]           differences. ATBC,       vegetables per day
                         significant             have an average
                    differences (p = 0.01)        78.64% LINE-1
                     between <690.9 grams       methylation%, and
                    of vegetables per day        >690.6 grams of
                     and >690.6 grams of       vegetables per day
                      vegetables per day         have an average
                                                  78.90% LINE-1
                                                  methylation%
Liao et al.,            No differences
  2011 [30]
Martin-Nunez et       LINE-1 methylation      The control group had
  al., 2014 [51]       increased in the        66.8 average LINE-1
                       control group (p       methylation% and the
                         = 0.001) but          intervention group
                       decreased in the         had 63.6 average
                      Mediterranean diet       LINE-1 methylation%
                      intervention group         after one year.
                         (p = 0.003)            Adjusted for age,
                                                 gender, BMI at
                                                    baseline
Tajuddin et al.,        No differences       Adjusted for age, sex,
  2013 [47]                                  region, smoking status
Zhang et al.,           No differences
  2012 [37]

Fruit

Agodi et al.,            Significant         Data given in tertiles
  2015 [21]            differences (p =       of methylation; women
                     0.022) between fruit      with <201 grams/day
                    intake groups of <201    fruit intake had lower
                      grams/day and <201         average LINE-1
                          grams/day             methylation% than
                                                 women with >201
                                                 grams/day fruit
                                                     intake
Hou et al.,             No differences
  2010 [27]
Karami et al.,             PLCO, No
  2015 [29]           differences. ATBC,
                        No differences
Tajuddin et al.,        No differences          Adjusted for age,
  2013 [47]                                   sex, region, smoking
                                                     status
Zhang et al.,           No differences
  2012 [37]

Folate

Agodi et al.,            Significant              Data given in
  2015 [21]              differences               tertiles of
                     (p = 0.027) between       methylation; women
                     folate deficient and          with folate
                     non-folate deficient        deficiency had
                            groups                lower average
                                               LINE-1 methylation%
                                               than women without
                                                folate deficiency
Bae et al.,              Significant          Women in "highest RBC
  2014 [52]              differences            folate group" had
                       (p = 0.05) among       5.12 baseline LINE-1
                     different levels of        methylation% and
                          RBC folate            women in "lowest
                                                RBC folate group"
                                                had 4.99 baseline
                                               LINE-1 methylation%
Gomes et al.,           No differences
  2012 [26]
Hou et al.,             No differences
  2010 [27]
Hsiung et al.,          No differences          Adjusted for age,
  2007 [28]                                    sex, race, smoking,
                                                  alcohol, HPV
                                                 serology, MTHFR
Karami et al.,             PLCO, No
  2015 [29]           differences. ATBC,
                        No differences
Perng et al.,           No differences
  2014 [34]
Tajuddin et             No differences          Adjusted for age,
  al., 2013 [47]                                   sex, region
Xu et al.,              No differences
  2012 [11]
Zhang et al.,           No differences
  2011 [36]
Zhang et al.,            Significant           Dietary folate from
  2012 [37]            differences (p =       fortified foods, /.(g
                         0.007) among          /1,000 kj spearman
                     different levels of           value 0.21
                     dietary folate from
                       fortified foods
Moore et al.,           No differences
  2008 [44]

Menopause status

Xu et al.,              No differences
  2012 [11]
Choi et al.,            No differences
  2009 [1]

Fetal Birthweight

Michels et al.,          Significant            "Low birthweight,
  2011 [53]          differences between      <2500 g" had a -0.82
                      low birthweight (p        change in LINE-1
                      = 0.007) and high         methylation% and
                       birthweight (p =        "High birthweight,
                      0.036) compared to      4000+ g" had a -0.43
                      normal birthweight        change in LINE-1
                           infants                methylation%

Family history
of breast cancer

Brennan et al.,         No differences
  2012 [9]
Delgado-Cruzata         No differences
  et al.,
  2014 [54]
Wu et al.,              No differences
  2011 [55]
Xu et al.,              No differences
  2012 [11]
Wu et al.,               Significant           Family history had
  2011 [55]         differences (p < 0.05)      151.4 average Alu
                    between family history     methylation% while
                    and no family history       no family history
                                                had 169.8 average
                                                Alu methylation%
Choi et al.,            No differences
  2009 [1]

Age at Menarche

Choi et al.,            No differences
  2009 [1]

Age at first
birth

Choi et al.,            No differences
  2009 [1]

Parity

Choi et al.,            No differences
  2009 [1]

Hormone Cycle

El-Maarri et            No differences
  al., 2011 [25]

Sex Hormones

Iwasaki et al.,         No differences
  2012 [56]
Ulrich et al.,          No differences
  2012 [57]

Hormone use

Choi et al.,            No differences
  2009 [1]
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Author:Chopra-Tandon, Nayha; Wu, Haotian; Arcaro, Kathleen F.; Sturgeon, Susan R.
Publication:Journal of Cancer Epidemiology
Article Type:Report
Date:Jan 1, 2017
Words:11463
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