Skip to main content
PMC home page
Lippincott Open Access logoLink to Lippincott Open Access
. 2017 Sep 22;26(6):511–520. doi: 10.1097/CEJ.0000000000000310

Adiposity, breast density, and breast cancer risk: epidemiological and biological considerations

Ludivine Soguel a,c,g, Francine Durocher b,e, André Tchernof e,f, Caroline Diorio a,c,d,✉
PMCID: PMC5627530  PMID: 27571214

Supplemental Digital Content is available in the text.

Keywords: adiposity, anthropometry, breast neoplasms, mammographic density, risk factors

Abstract

Excess total body fat and abdominal adipose tissue are recognized risk factors for metabolic diseases but also for some types of cancers, including breast cancer. Several biological mechanisms in connection with local and systemic effects of adiposity are believed to be implicated in breast cancer development, and may involve breast fat. Breast adipose tissue can be studied through mammography by looking at breast density features such as the nondense area mainly composed of fat, or the percent breast density, which is the proportion of fibroglandular tissue in relation to fat. The relation between adiposity, breast density features, and breast cancer is complex. Studies suggest a paradoxical association as adiposity and absolute nondense area correlate positively with each other, but in contrast to adiposity, absolute nondense area seems to be associated negatively with breast cancer risk. As breast density is one of the strongest risk factors for breast cancer, it is therefore critical to understand how these factors interrelate. In this review, we discuss these relations by first presenting how adiposity measurements and breast density features are linked to breast cancer risk. Then, we used a systematic approach to capture the literature to review the relation between adiposity and breast density features. Finally, the role of adipose tissue in carcinogenesis is discussed briefly from a biological perspective.

Introduction

Breast cancer is the most common cancer in women worldwide, accounting for 25% of the total new cases in 2012 and affecting 1.67 million women (Ferlay et al., 2014). With 522 000 estimated deaths in 2012, it is one of the leading causes of mortality due to cancer in women worldwide (Ferlay et al., 2014).

Excess adiposity is an established risk factor for breast cancer among postmenopausal women and both total body fatness and abdominal body fat distribution seem to play a role (World Cancer Research Fund, American Institute for Cancer Research, 2010). The association is less consistent in premenopausal women, for whom the underlying biological mechanisms remain undetermined (Anderson and Neuhouser, 2012; Cecchini et al., 2012; Cheraghi et al., 2012; Amadou et al., 2013; Pierobon and Frankenfeld, 2013; Emaus et al., 2014). Furthermore, the role of breast fat in breast cancer development is not well understood as yet. Actually, fat tissue has been described as a microenvironment promoting carcinogenesis through different mechanisms, in particular, chronic inflammation (Park et al., 2014; Perez-Hernandez et al., 2014), but it also has a potentially protective role, especially as a source of vitamin D (Narvaez et al., 2014).

The relation between fat tissue located in the breast and breast cancer risk can be studied through mammographic density features. Indeed, the absolute nondense area reflects breast fat tissue and, inversely, percent density represents the proportion of nonfat tissue in the breast. Percent density is one of the strongest identified risk factors for breast cancer and is used frequently as a surrogate for breast cancer risk in epidemiological studies (Boyd et al., 2007). The apparent paradox in the relation between adiposity, breast density, and breast cancer risk is that adiposity and absolute nondense area are correlated positively but, even though adiposity is a recognized risk factor for breast cancer, there is some indication that absolute nondense area is associated negatively with breast cancer risk (Pettersson et al., 2014).

Adiposity can be assessed or estimated using various techniques, and can thus be described with a number of different markers. In addition, breast density can also be defined with many variables such as percent density, absolute dense, and nondense areas or volumes. The relation between adiposity, breast density features, and breast cancer risk can therefore be evaluated using many different approaches. In this article, we aim to discuss these relations from an epidemiological and biological point of view. We will first present currently used techniques of measurement and markers of adiposity and describe their association with breast cancer risk. Similarly, for breast density features, we will describe these measurements and their associations with breast cancer risk. We will then review the link between adiposity and breast density features. Finally, we will briefly address the potential role of adipose tissue in carcinogenesis.

Adiposity

Adiposity can be described with several markers to reflect body fatness or body fat distribution.

Body fatness

Body fatness can be assessed or estimated using many different techniques (Gibson, 2005a, 2005b; Willett, 2013). The most valid methods are dual-energy X-ray absorptiometry and densitometry, although they are rarely used in epidemiology. Dual-energy X-ray absorptiometry enables the measurement of total body fat, which can also be expressed in percent body fat, by excluding bone mineral mass and fat-free mass. Densitometry enables the calculation of percent body fat from the weight of a patient in the air and under water, or with the easier air displacement plethysmography technique (Siri, 1961). Percent body fat can also be estimated by bioelectrical impedance, an inexpensive and noninvasive technique based on the difference in conductance and resistance of fat and lean tissue (Lukaski et al., 1986), or extrapolated from an evaluation of subcutaneous fat performed by the measurement of skinfold(s) thickness with a simple caliper (Durnin and Womersley, 1974).

Several obesity indices have also been developed to characterize body fatness. The one that is universally used, both in epidemiology and in the clinical setting, is BMI, which is weight (kg) divided by squared height (m2) (Quetelet, 1869). This index has the advantage of being poorly correlated with height and rather highly correlated with total body fat (Willett, 2013). However, it remains an indirect measure of body fatness that could be influenced by various factors such as age or body proportions in addition to body composition (Heymsfield et al., 2004). The use of BMI as an estimation of body fatness has been debated widely among scientists. However, from a public health perspective, even if the correlation between BMI and total body fat is not ideal [the correlation coefficient is about 0.75 (Willett, 2013)], WHO recommends this easy-to-use marker to characterize individuals presenting with a similar level of health risk. According to WHO, overweight and obesity are defined as a BMI between greater than or equal to 25 and less than 30 kg/m2 and a BMI greater than or equal to 30 kg/m2, respectively (WHO Expert Committee on Physical Status, 1995; WHO Consultation on Obesity, 2000; WHO Expert Consultation, 2004). Somatotypes, which are, images representing silhouettes of different BMI ranging from very thin to obese, such as those developed first by Stunkard et al. (1983), have also been used in epidemiological studies for self-reported body shape perception. The validity of such tools varies mainly according to sex, age, or ethnicity. The correlations between self-perceived body shape and a measure of body fatness, such as BMI for example, range from 0.56 to 0.99 in the adult general population (Gardner and Brown, 2010). Of importance is that many tools are not validated for use with obese individuals. As in this specific population perceived body shape can be subjected to distortion, special attention should be paid to the choice of a measurement instrument (Pull and Aguayo, 2011).

The dynamic of weight change throughout adulthood provides further information on body fatness. In healthy individuals exposed to chronic overfeeding, it has been shown that the gain and the composition of body mass vary individually, but on average, 2 kg of fat mass is stored for 1 kg of fat-free mass (Bouchard et al., 2014). Weight gain can be predicted by the positive energy imbalance between total energy intake and total energy expenditure minus the energy needed for energy storage in the tissues (Christiansen et al., 2005; Sorensen, 2009). Despite the apparent simplicity of this equation, its components are still not fully understood. Major challenges relate to technical issues and metabolic considerations. Indeed, precise measurement of energy expenditure and intake in real-life conditions and over a long period is extremely difficult (Schutz et al., 2014). Furthermore, the equation described above is a dynamic process and its different components influence the others: weight gain will increase energy expenditure (Christiansen et al., 2005) and storage efficiency varies (Schutz et al., 2014). Another issue relates to the drivers of weight gain or fat mass accretion throughout adult life. This complex question has been the topic of many studies and the underlying mechanisms are not yet fully elucidated. For example, the classical idea that increased intake and decreased energy expenditure are the causes of weight gain leading to obesity needs to be considered in the context of the theoretical possibility that induction of weight gain occurs through active lipid accretion within adipose tissue itself, in particular through a tendency for increased lipid storage (Sorensen, 2009).

Body fat distribution

Several methods are available to measure or estimate the distribution of body fat, with the main objective to distinguish peripheral from abdominal fat and to further discriminate visceral fat from subcutaneous fat. Visceral fat can be assessed by imaging techniques such as computed tomography scan or MRI. However, as these techniques are expensive and not easily available, proxy measures such as waist circumference (WC) and waist-to-hip ratio (WHR) have been used extensively in clinical settings and epidemiological studies. WHR is WC divided by hip circumference and distinguishes fatness in the lower trunk from fatness in the upper trunk (Gibson, 2005a, 2005b). WC and WHR are correlated strongly with total body fat (Gibson, 2005a, 2005b) and reflect both total and regional fatness (Willett, 2013). However, WC has been found to be a better surrogate for abdominal fat as it is more strongly correlated with total abdominal fat and with visceral fat, as assessed by computed tomography scan, than WHR (Pouliot et al., 1994; Clasey et al., 1999). As WC is also associated with body size, WHR and waist-to-height ratio have been considered by some as markers of abdominal adiposity partly adjusted for body size. Distribution of body fat is a critical variable when considering health risk. Indeed, many epidemiological studies have shown that abdominal obesity, particularly excess visceral fat accumulation on anatomical structures such as the mesentery and greater omentum, is a strong risk factor for chronic diseases such as diabetes, cardiovascular diseases, or cancers (Tchernof and Despres, 2013). WC, WHR, and waist-to-height ratio have been shown to be correlated with the metabolic syndrome, cardiometabolic risk (Sun et al., 2010; Kodama et al., 2012; Borne et al., 2015; Nazare et al., 2015), and cancer risk, including breast cancer (World Cancer Research Fund, American Institute for Cancer Research, 2010; Vongsuvanh et al., 2013).

Adiposity and breast cancer risk

Studying the association between adiposity and a specific pathology is a challenge as the marker chosen to reflect adiposity can, per se, be a cause of heterogeneity in the results. All of the parameters described above have been used in studies on breast cancer risk, but the most commonly used are BMI, WC, WHR, and weight gain (World Cancer Research Fund, American Institute for Cancer Research, 2010). Among postmenopausal women, positive associations with breast cancer risk have been found for BMI (convincing evidence), weight gain during adulthood, WC, and WHR (probable evidence) (World Cancer Research Fund, American Institute for Cancer Research, 2010). These associations are less consistent in premenopausal women in whom a protective effect of body fatness has been described (World Cancer Research Fund, American Institute for Cancer Research, 2010). However, the underlying pathophysiological mechanisms are not understood as yet and the protective effect is currently questioned by contrary results, indicating possible differences according to the type of cancer, ethnicity, risk level of breast cancer in the population under study, and other methodological factors (Anderson and Neuhouser, 2012). For instance, in a cohort of women at high breast cancer risk in North America including 5864 premenopausal women, Cecchini et al. (2012) found an increased risk of invasive breast cancer among premenopausal women within increasing categories of BMI, and a meta-analysis carried out by Pierobon and Frankenfeld (2013) showed a clearly positive association between obesity (BMI>30 kg/m2) and the risk of triple-negative breast cancer status among premenopausal women. Furthermore, a recent publication from the EPIC cohort (European Prospective Investigation into Cancer and Nutrition) reported a positive association between weight gain during middle adulthood and breast cancer, especially for women aged 50 years or younger (Emaus et al., 2014). Ethnicity is also important. Indeed, in their meta-analysis, Amadou et al. (2013) showed that after stratification by ethnicity, BMI was significantly and inversely associated with breast cancer risk only among African and White premenopausal women, but not among Asian premenopausal women, in whom it was associated positively with breast cancer risk. With respect to body fat distribution, they showed that WHR, but not WC, was associated positively with breast cancer risk in premenopausal women and that this association was stronger among Asian women.

Body fatness and body fat distribution seem to represent significant determinants of breast cancer risk, particularly among postmenopausal women. Considering that fat is also a major component of the breast, what about its local role in breast cancer risk? Macroscopically, this relation can be studied through breast density features, of which percent density is a well-recognized risk factor for breast cancer (Huo et al., 2014).

Breast density features and breast cancer risk

Determination of breast density is based on the radiological properties of breast tissue and reflects breast tissue composition. Indeed, fibroglandular tissue attenuates on radiography more than adipose tissue and appears white on a mammogram. On this basis, breast density can be assessed quantitatively or qualitatively by several methods (Assi et al., 2012). Nowadays, quantitative assessment, usually computer assisted, is the most frequently used in research. However, qualitative methods with visual categorization are also used, for example in the classification proposed by Wolfe (1976) and in the Breast Imaging-Reporting and Data System (American College of Radiology, 2014). Quantitatively, three density features are mainly considered: the absolute dense area (mainly fibroglandular tissue), the absolute nondense area (mainly adipose tissue), and the percent density, which is the proportion of dense tissue in the total breast area. Recently, three-dimensional techniques have been developed to assess total and absolute volumes, but most are still in the process of validation.

With respect to breast cancer risk, percent density is the most studied mammographic density feature. It has constantly been shown to be strongly and positively associated with breast cancer risk among premenopausal and postmenopausal women (Kato et al., 1995; Nagata et al., 2005; Boyd et al., 2009; Pettersson et al., 2014). In a meta-analysis, McCormack and dos Santos Silva (2006) calculated that a breast density of at least 75% was associated with a four-fold relative risk of breast cancer compared with a breast density less than 5%. Absolute dense area has also been studied by many research groups and was found to be associated positively with breast cancer risk (Kato et al., 1995; Nagata et al., 2005; Boyd et al., 2009; Pettersson et al., 2014). Some authors examined whether volume measurements were better predictors of breast cancer risk and found very similar results overall (Boyd et al., 2009; Shepherd et al., 2011). However, the association between volumetric measures of breast density and breast cancer risk still needs confirmation.

The relation between absolute nondense area and breast cancer risk has seldom been studied. To gather the maximum number of studies, we used a systematic method within the PubMed library. We developed and combined the concepts ‘nondense’ and ‘breast cancer’ (see Supplementary Text, Supplemental digital content 1, http://links.lww.com/EJCP/A115, which presents the search strategy). Among the 118 titles retrieved from the search strategy, six original studies and one meta-analysis presented the relation between nondense breast tissue and breast cancer, and are discussed below. One more study (Torres-Mejia et al., 2005) was added by reviewing bibliographies.

Paradoxically, despite the fact that adiposity has been shown to be associated positively with breast cancer risk (World Cancer Research Fund, American Institute for Cancer Research, 2010), several studies (Torres-Mejia et al., 2005; Pettersson et al., 2011; Olson et al., 2012; Yaghjyan et al., 2013; Baglietto et al., 2014; Pettersson et al., 2014), although not all (Stone et al., 2010; Lokate et al., 2011), have found a negative association between the amount of adipose tissue in the breast, reflected by the absolute nondense area, and breast cancer risk. It is only recently that absolute nondense area has been considered in studies on breast cancer risk. Recently, Pettersson et al. (2014) had the opportunity to reanalyze the pooled data of 13 case–control (including 12 nested within cohorts) studies from the DENSNP consortium collaborators, an international network of epidemiological studies on breast density and genetic variants that includes 1776 case patients and 2834 control participants. They found that absolute nondense area was strongly and negatively associated with breast cancer risk in models adjusted for usual confounders including BMI, and this result was maintained after controlling for absolute dense area with an OR per 1-SD increase of 0.82 [95% confidence interval (CI) 0.71–0.94] and 0.85 (95% CI 0.75–0.96) for premenopausal and postmenopausal women, respectively. Similarly, in a prospective cohort not included in this meta-analysis that included 3211 healthy UK women, with 111 developing breast cancer during a median follow-up time of 15 years, Torres-Mejia et al. (2005) found that absolute nondense area was associated negatively with breast cancer risk in analyses adjusted for usual confounders including BMI and 10-year change in BMI (hazard ratio per 1-SD increase, 0.76; 95% CI 0.57–1.02). In a matched case–control study nested within cohorts recruited at screening mammography in the UK and including 634 cases and 1880 controls, Stone et al. (2010) also found a negative association between absolute nondense area and breast cancer risk, but this association was lost after adjusting for absolute dense area (Q5 vs. Q1 OR 1.08; 95% CI 0.79–1.49). Unfortunately, the analyses carried out in the latter study could not be adjusted for usual confounders or for BMI as these data were not available. However, in a case–control study nested in the EPIC cohort of the Netherlands including 358 postmenopausal breast cancer cases and 859 postmenopausal controls, Lokate et al. (2011) found no association between absolute nondense area and breast cancer risk in analyses adjusted for usual confounders including BMI, whereas a positive association was observed when they further adjusted for absolute dense area (Q5 vs. Q1 OR 2.4; 95% CI 1.3–4.2). The technique used to measure mammographic density was very similar between studies: a computer-assisted method (mainly cumulus) on digitized film, but it has been hypothesized that the divergent results found by Lokate and colleagues could be partly due to the mammographic view chosen to evaluate absolute nondense area. Indeed, the mediolateral oblique projection used is more likely to overestimate nondense area because of the inclusion of some subcutaneous fat in the breast adipose tissue, and the evaluated risk could be partly attributed to adiposity instead of breast nondense area (Shepherd and Kerlikowske, 2012). Overall, studies are inconsistent even if a protective effect of breast fat seems to emerge. Many studies remain to be carried out and a better understanding of the association between adiposity and breast density could help increase understanding of the relation between adiposity and breast cancer risk.

Adiposity and breast density features

To review the relation between adiposity and breast density features, we used a systematic approach within the PubMed library. We developed and combined the concepts ‘adiposity’ and ‘breast density’ (see Supplementary Text, Supplemental digital content 2, http://links.lww.com/EJCP/A116, which presents the search strategy). Among the 3028 titles retrieved from the search strategy, 31 original studies were included in our review as they presented the association between one measure of adiposity and at least one absolute measure of breast density or presented the association between weight or BMI change and breast density (see Supplementary Fig., Supplemental digital content 3, http://links.lww.com/EJCP/A117, which present the process of selection in a PRISMA flow chart).

Static measures of adiposity and breast density features

In contrast to the inconsistent results for the association between absolute nondense area and breast cancer risk, cross-sectional analyses on the association between absolute nondense area, or volume, and adiposity have shown a constant positive association for BMI (Boyd et al., 1998; Maskarinec et al., 2001; Heng et al., 2004; Haars et al., 2005; Boyd et al., 2006; Guthrie et al., 2007; Irwin et al., 2007; Stone et al., 2009; Sung et al., 2010; Harris et al., 2011; Tseng and Byrne, 2011; Woolcott et al., 2011; Eng et al., 2014; Nayeem et al., 2014; Schetter et al., 2014; Soguel and Diorio, 2016), with correlation coefficients ranging from 0.41 to 0.77, percentage (Boyd et al., 1998; Sung et al., 2010; Woolcott et al., 2011) or total (Nayeem et al., 2014) body fat, and WC or WHR (Sung et al., 2010; Tseng and Byrne, 2011; Woolcott et al., 2011; Nayeem et al., 2014; Soguel and Diorio, 2016). These results are not surprising because when adiposity is high, it can be expected that breast fat is also found in high amounts. However, it strengthens the apparent paradox in the relation between breast cancer risk and adipose tissue depending on its localization. Indeed, absolute nondense area, reflecting breast adipose tissue, and body fatness are associated positively with one another, but they seem to have opposite associations with breast cancer risk.

Percent density, whether calculated with areas or volumes or estimated by subjective visual scales, has been consistently and negatively associated with measures of body fatness such as BMI (Boyd et al., 1998; Maskarinec et al., 2001; Heng et al., 2004; Haars et al., 2005; Tamimi et al., 2005; Boyd et al., 2006; Ursin et al., 2006; Guthrie et al., 2007; Irwin et al., 2007; McCormack et al., 2007; Jeffreys et al., 2008; McCormack et al., 2008; Reeves et al., 2009; Stone et al., 2009; Lokate et al., 2010; Sung et al., 2010; Harris et al., 2011; Maskarinec et al., 2011; Tehranifar et al., 2011; Tseng and Byrne, 2011; Woolcott et al., 2011; Dorgan et al., 2012; Pollan et al., 2012; Eng et al., 2014; Gierach et al., 2014; Nayeem et al., 2014; Schetter et al., 2014; Soguel and Diorio, 2016), with correlation coefficients ranging from −0.41 to −0.61, percentage (Boyd et al., 1998; Caire-Juvera et al., 2008; Sung et al., 2010; Woolcott et al., 2011) or total (Nayeem et al., 2014) body fat, or estimated by somatotypes (Samimi et al., 2008) and with measures of abdominal body fat distribution such as WC or WHR (Sung et al., 2010; Tseng and Byrne, 2011; Woolcott et al., 2011; Dorgan et al., 2012; Pollan et al., 2012; Nayeem et al., 2014; Soguel and Diorio, 2016). Again, this is not surprising. Indeed, because the absolute nondense area is usually the main component of the breast among women included in mammographic studies and because percent density is the proportion of dense area in the whole breast including fat, percent density is expected to be associated negatively with absolute nondense area and thus with adiposity. However, here again, as both percent density and adiposity are positively associated with breast cancer risk but negatively associated with one another, an apparent paradox arises in the link between adiposity, breast density, and breast cancer risk.

The association between absolute dense area and adiposity is less clear and cross-sectional analyses have led to inconsistent results (Boyd et al., 1998; Maskarinec et al., 2001; Heng et al., 2004; Haars et al., 2005; Tamimi et al., 2005; Boyd et al., 2006; Ursin et al., 2006; Guthrie et al., 2007; Irwin et al., 2007; McCormack et al., 2007; McCormack et al., 2008; Reeves et al., 2009; Stone et al., 2009; Lokate et al., 2010; Sung et al., 2010; Harris et al., 2011; Tehranifar et al., 2011; Tseng and Byrne, 2011; Woolcott et al., 2011; Eng et al., 2014; Gierach et al., 2014). Breast density features were mainly obtained by the same technique, that is, computer-assisted threshold method (mainly cumulus) on digitized film, and we observed no differences in the association according to the method used. Therefore, the observed discrepancies remain unexplained, but could be caused by important variations in the adjustments performed in the analyses and, to a certain extent, to differences in the population under study, including ethnicity or menopausal status. To show the complexity of available studies, we propose to detail the association between absolute dense area and BMI according to the variables included in the models. In unadjusted analyses or in those adjusted at least for age, studies have shown negative (Boyd et al., 1998; Tamimi et al., 2005; Boyd et al., 2006; Stone et al., 2009; Lokate et al., 2010; Sung et al., 2010; Harris et al., 2011; Gierach et al., 2014), positive (Heng et al., 2004; Reeves et al., 2009; Tseng and Byrne, 2011), or no association (Haars et al., 2005; Ursin et al., 2006; Irwin et al., 2007; Tehranifar et al., 2011). In analyses considering adjustment for the usual potential confounders, but not for any other variables that further characterize adiposity, studies have shown negative (Maskarinec et al., 2001; Stone et al., 2009; Sung et al., 2010; Woolcott et al., 2011; Eng et al., 2014), positive (Tseng and Byrne, 2011), or no association (Guthrie et al., 2007; McCormack et al., 2007; McCormack et al., 2008; Tehranifar et al., 2011). Finally, when the analyses were adjusted for usual confounders and for at least one variable that characterizes body fatness (percent body fat) or body fat distribution (WC, WHR), in the few remaining studies, the association was found to be positive (Heng et al., 2004; Tseng and Byrne, 2011) or null (Haars et al., 2005).

Interestingly, when absolute dense volume is considered instead of area, a positive association between BMI and absolute dense volume has been found in several studies (McCormack et al., 2007; Warren et al., 2007; Jeffreys et al., 2008; Lokate et al., 2010; Eng et al., 2014; Gierach et al., 2014; Schetter et al., 2014), although not all (Woolcott et al., 2011; Dorgan et al., 2012; Nayeem et al., 2014). As volume measurement techniques are still in development and as results vary from one technique to another (Shepherd et al., 2011; Nayeem et al., 2014), further studies are needed to evaluate the relation between adiposity, absolute dense volume, and breast cancer risk. Indeed, in the studies presented here, different techniques have been used to assess breast density features [standard mammogram form, single X-ray absorptiometry, MRI, Volpara (Volpara Health Technologies, Wellington, New Zealand); Quantra (Hologic, Bedford, Massachusetts, USA)], and although the two studies using MRI (Dorgan et al., 2012; Nayeem et al., 2014) did not show a positive association between absolute dense volume and BMI, it is difficult to draw conclusions on the impact of these techniques. However, the positive association found between BMI and absolute dense volume could suggest that volume measurements provide additional information on breast density and its relation to breast cancer risk (Lokate et al., 2010). Indeed, some authors have discussed the fact that, according to the technique used, absolute dense volume reflects not only fibroglandular mass but also part of the fat mass of the breast because of the water content of the latter (Shepherd et al., 2011; Gierach et al., 2014). The associations evaluated with these approaches could be compared with analyses carried out on area measurements partly adjusted for nondense area or for adiposity. This hypothesis is also reported in a recent analysis that we carried out among 1435 premenopausal and postmenopausal White women recruited at screening mammography (Soguel and Diorio, 2016). We computed correlations between BMI, WC, WHR, and absolute dense area and found a negative correlation after adjustment for potential confounders, but not for variables reflecting adiposity (r=−0.21; −0.23; and −0.19, respectively, P<0.0001). When we further adjusted for absolute nondense area, that is, when breast fat is considered, these three correlations switched from negative to positive (r=0.16, 0.12, and 0.06, respectively, P<0.025) (Soguel and Diorio, 2016). To our knowledge, this is the only study in which the association between adiposity and absolute dense area was adjusted for absolute nondense area.

Dynamic measures of adiposity and breast density features

The association between adult weight gain and breast density features has seldom been studied. In contrast to the negative association found with static measures of adiposity, adult weight gain was found to be associated positively with percent density in two studies (Pollan et al., 2012; Soguel and Diorio, 2016) (women who had gained more than 25 kg showed an increase>8.5%): first, in a study among 3584 premenopausal and postmenopausal women recruited at screening mammography in Spain, in which Pollan et al. (2012) found a positive association in analyses adjusted for usual confounders including BMI and WHR, and second, in our recent large study, in which we found a positive association between adult weight gain and percent density after adjusting for usual confounders including BMI and WHR (Soguel and Diorio, 2016). Moreover, in this study, BMI variation was also positively associated with percent breast density. However, two studies found a negative association, but without adjusting for current adiposity. Thus, this finding could be due to residual confounding, as shown by Samimi et al. (2008) in a study among 1398 premenopausal and postmenopausal US women from the Nurses’ Health Study, and by Tseng and Byrne (2011) among 415 US Chinese premenopausal and perimenopausal women. However, in the latter study, the association was lost after adjustment for adiposity (BMI residuals and WC).

The association between weight gain during adulthood and absolute dense or nondense areas is poorly understood. To our knowledge, only two studies have assessed these associations: Tseng and Byrne (2011) evaluated this among premenopausal and perimenopausal Chinese women and our group among premenopausal and postmenopausal White women (Soguel and Diorio, 2016). In terms of absolute dense area, both studies showed a positive association with adult weight gain in models adjusted for adiposity as described above (women who had gained more than 25 kg showed an increase>7.7 cm2). Tseng and Byrne (2011) found a positive association, especially in women with a BMI<23 kg/m2, and so did we, especially among women with a high nondense area (Soguel and Diorio, 2016). In our study, BMI variation was also found to be associated positively with absolute dense area (Soguel and Diorio, 2016). With respect to absolute nondense area, in contrast to what was observed with static measures of adiposity, we observed a negative association with weight gain that was present among women with low and high absolute dense area in models that included BMI and WHR (Soguel and Diorio, 2016). Meanwhile, in models that included BMI (residuals) and WC, Tseng and Byrne (2011) found no association between adult weight gain and absolute nondense area among all women or by BMI strata.

A few longitudinal studies have examined the association between short-term weight change and breast density features (Boyd et al., 1997; Guthrie et al., 2007; Reeves et al., 2009; Woolcott et al., 2011; Wanders et al., 2015). However, the short follow-up period and small variation in weight or BMI (Boyd et al., 1997; Reeves et al., 2009; Woolcott et al., 2011; Wanders et al., 2015), the changes in menopausal status during follow-up (Guthrie et al., 2007; Reeves et al., 2009; Wanders et al., 2015), and the weight loss intent of the study (Boyd et al., 1997; Woolcott et al., 2011) are major limitations to consider in a comparison with studies on weight gain during adulthood. These studies are not discussed here.

In summary, it is not surprising that static measures of adiposity are associated positively with absolute nondense area. Therefore, the negative association observed with percent density is expected. The association with absolute dense area or volume also seems to be positive, but for area, it seems to depend on the adjustments that are performed in the analysis. Considering adult weight gain, when current adiposity is considered in the models, the association with percent density or absolute dense area seems to be positive, but for the latter, it could be limited to some subgroups. The association with absolute nondense area has been scarcely studied and is still controversial.

The association between static or dynamic measures of adiposity and breast density features has not been elucidated as yet and a number of questions remain. However, dynamic measures of adiposity could better reflect what occurs in term of risk. Indeed, associations evaluated in cross-sectional studies do not address causation and cannot simply be interpreted as a risk. Furthermore, the dynamic measure presumably reflects a long-term exposure to a risk factor that is critical for cancer development. Current knowledge does not enable a straightforward understanding of the apparent paradox of the role of fat tissue in breast cancer development when considering fat in the whole body or in the breast.

Role of adipose tissue in breast carcinogenesis

In the breast, histologically fibroglandular tissue (dense area) is mainly constituted by epithelial and stromal cells and adipose tissue (nondense area) mainly of adipocytes. Fibroglandular tissue represents the population of breast cells at risk of carcinogenic transformation (Trichopoulos et al., 2005). Breast cancer generally arises in epithelial cells (Russo et al., 1990) and an increase in overall cellular mass is believed to increase the risk of breast cancer (Preston-Martin et al., 1990). However, breast adipose tissue, as part of the microenvironment surrounding the fibroglandular zone, seems to considerably influence the epithelial differentiation and proliferation by different mechanisms, not all elucidated as yet (Pettersson and Tamimi, 2012). In particular, it has been shown to contribute toward the development and progression of breast tumors in coculture experiments and animal models (Wang et al., 2012). Moreover, fat tissue dysfunction is considered to generate local and peripheral chronic low-grade inflammation, sex hormone alterations, and insulin resistance (Lumeng et al., 2007; Park et al., 2014; Perez-Hernandez et al., 2014). Inflammation in breast adipose tissue is currently believed to play an important role in the development of breast tumors (Santander et al., 2015). Furthermore, body fat, through its aromatase activity, is a significant source of endogenous estrogens, known to promote breast tissue proliferation (Kulendran et al., 2009). As adipose tissue is almost the exclusive source of estrogens among postmenopausal women, the impact of adiposity on breast cancer risk could be more pronounced in this population and one can hypothesize that this is a reason for the unclear results found among premenopausal women. Aromatase is also present in the breast tissue itself where stromal cells in the dense area, and adipocytes in the nondense area, were shown to present the highest aromatase immunoreactivity (Vachon et al., 2011). It is likely that a large breast adipose tissue accumulation accounts for a non-negligible source of local endogen estrogen and, therefore, could contribute toward breast cancer risk. Although, to our knowledge, only one study has evaluated the association between adult weight gain and absolute dense area according to nondense area, this hypothesis is well illustrated by the fact that weight gain was associated positively with absolute dense area, especially if breast fat was present in larger amounts (Soguel and Diorio, 2016).

However, a potentially protective influence of breast fat has also been described. The ability of fat to store vitamin D, known to exert a protective effect against breast cancer development, could partly explain the protective effect (Narvaez et al., 2014). Involution has also been suggested to play a role through the reduction of fibroglandular tissue in favor of adipose tissue (Pettersson et al., 2014). The protective effect of breast fat is plausible because when nondense area increases, breast cancer risk seems to decrease (Pettersson et al., 2014) and when body weight increases during adulthood, breast fat has been shown to decrease or remain stable (Tseng and Byrne, 2011; Soguel and Diorio, 2016).

Conclusion

The association between breast fat and breast cancer risk deserves further epidemiological and biological investigation. The challenges faced when comparing studies lead us to advocate the need for longitudinal (cohort) studies assessing the impact of body weight history through adulthood on breast density features and breast cancer risk over a long follow-up among premenopausal women and among postmenopausal women separately to avoid specific changes during the menopause transition.

Supplementary Material

SUPPLEMENTARY MATERIAL
cej-26-511-s001.pdf (137.5KB, pdf)
cej-26-511-s002.pdf (137KB, pdf)
cej-26-511-s003.pdf (182.3KB, pdf)

Supplemental digital content is available for this article. Direct URL citations appear in the printed text and are provided in the HTML and PDF versions of this article on the journal's website (www.eurjcancerprev.com).

Acknowledgements

C.D. is a recipient of The Canadian Breast Cancer Foundation-Canadian Cancer Society Capacity Development award (award #703003) and The Fonds de Recherche du Québec-Santé (FRQS) Research Scholar. L.S. received a scholarship from the Cancer Research Center at Laval University, Québec.

Conflicts of interest

There are no conflicts of interest.

References

  1. Amadou A, Ferrari P, Muwonge R, Moskal A, Biessy C, Romieu I, Hainaut P. (2013). Overweight, obesity and risk of premenopausal breast cancer according to ethnicity: a systematic review and dose-response meta-analysis. Obes Rev 14:665–678. [DOI] [PubMed] [Google Scholar]
  2. American College of Radiology (2014). ACR BI-RADS Mammography Breast Imaging Atlas, 5th ed Reston, VA: American College of Radiology. [Google Scholar]
  3. Anderson GL, Neuhouser ML. (2012). Obesity and the risk for premenopausal and postmenopausal breast cancer. Cancer Prev Res (Phila) 5:515–521. [DOI] [PubMed] [Google Scholar]
  4. Assi V, Warwick J, Cuzick J, Duffy SW. (2012). Clinical and epidemiological issues in mammographic density. Nat Rev Clin Oncol 9:33–40. [DOI] [PubMed] [Google Scholar]
  5. Baglietto L, Krishnan K, Stone J, Apicella C, Southey MC, English DR, et al. (2014). Associations of mammographic dense and nondense areas and body mass index with risk of breast cancer. Am J Epidemiol 179:475–483. [DOI] [PubMed] [Google Scholar]
  6. Borné Y, Nilsson PM, Melander O, Hedblad B, Engström G. (2015). Multiple anthropometric measures in relation to incidence of diabetes: a Swedish population-based cohort study. Eur J Public Health 25:1100–1105. [DOI] [PubMed] [Google Scholar]
  7. Bouchard C, Tchernof A, Tremblay A. (2014). Predictors of body composition and body energy changes in response to chronic overfeeding. Int J Obes (Lond) 38:236–242. [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Boyd NF, Greenberg C, Lockwood G, Little L, Martin L, Byng J, et al. (1997). Effects at two years of a low-fat, high-carbohydrate diet on radiologic features of the breast: results from a randomized trial. Canadian Diet and Breast Cancer Prevention Study Group. J Natl Cancer Inst 89:488–496. [DOI] [PubMed] [Google Scholar]
  9. Boyd NF, Lockwood GA, Byng JW, Little LE, Yaffe MJ, Tritchler DL. (1998). The relationship of anthropometric measures to radiological features of the breast in premenopausal women. Br J Cancer 78:1233–1238. [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Boyd NF, Martin LJ, Sun L, Guo H, Chiarelli A, Hislop G, et al. (2006). Body size, mammographic density, and breast cancer risk. Cancer Epidemiol Biomarkers Prev 15:2086–2092. [DOI] [PubMed] [Google Scholar]
  11. Boyd NF, Guo H, Martin LJ, Sun L, Stone J, Fishell E, et al. (2007). Mammographic density and the risk and detection of breast cancer. N Engl J Med 356:227–236. [DOI] [PubMed] [Google Scholar]
  12. Boyd N, Martin L, Gunasekara A, Melnichouk O, Maudsley G, Peressotti C, et al. (2009). Mammographic density and breast cancer risk: evaluation of a novel method of measuring breast tissue volumes. Cancer Epidemiol Biomarkers Prev 18:1754–1762. [DOI] [PubMed] [Google Scholar]
  13. Caire-Juvera G, Arendell LA, Maskarinec G, Thomson CA, Chen Z. (2008). Associations between mammographic density and body composition in Hispanic and non-Hispanic White women by menopause status. Menopause 15:319–325. [DOI] [PubMed] [Google Scholar]
  14. Cecchini RS, Costantino JP, Cauley JA, Cronin WM, Wickerham DL, Land SR, et al. (2012). Body mass index and the risk for developing invasive breast cancer among high-risk women in NSABP P-1 and STAR breast cancer prevention trials. Cancer Prev Res (Phila) 5:583–592. [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Cheraghi Z, Poorolajal J, Hashem T, Esmailnasab N, Doosti Irani A. (2012). Effect of body mass index on breast cancer during premenopausal and postmenopausal periods: a meta-analysis. PLoS One 7:e51446. [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Christiansen E, Garby L, Sørensen TI. (2005). Quantitative analysis of the energy requirements for development of obesity. J Theor Biol 234:99–106. [DOI] [PubMed] [Google Scholar]
  17. Clasey JL, Bouchard C, Teates CD, Riblett JE, Thorner MO, Hartman ML, Weltman A. (1999). The use of anthropometric and dual-energy X-ray absorptiometry (DXA) measures to estimate total abdominal and abdominal visceral fat in men and women. Obes Res 7:256–264. [DOI] [PubMed] [Google Scholar]
  18. Dorgan JF, Klifa C, Shepherd JA, Egleston BL, Kwiterovich PO, Himes JH, et al. (2012). Height, adiposity and body fat distribution and breast density in young women. Breast Cancer Res 14:R107. [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Durnin JV, Womersley J. (1974). Body fat assessed from total body density and its estimation from skinfold thickness: measurements on 481 men and women aged from 16 to 72 years. Br J Nutr 32:77–97. [DOI] [PubMed] [Google Scholar]
  20. Emaus MJ, van Gils CH, Bakker MF, Bisschop CN, Monninkhof EM, Bueno-de-Mesquita HB, et al. (2014). Weight change in middle adulthood and breast cancer risk in the EPIC-PANACEA study. Int J Cancer 135:2887–2899. [DOI] [PubMed] [Google Scholar]
  21. Eng A, Gallant Z, Shepherd J, McCormack V, Li J, Dowsett M, et al. (2014). Digital mammographic density and breast cancer risk: a case–control study of six alternative density assessment methods. Breast Cancer Res 16:439. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Ferlay J, Soerjomataram I, Dikshit R, Eser S, Mathers C, Rebelo M, et al. (2015). Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012. Int J Cancer 136:E359–E386. [DOI] [PubMed] [Google Scholar]
  23. Gardner RM, Brown DL. (2010). Body image assessment: A review of figural drawing scales. Pers Individ Dif 48:107–111. [Google Scholar]
  24. Gibson RS. (2005a). Anthropometric assessment of body composition. Principles of nutritional assessment, 2nd ed New York, NY: Oxford University Press; 273–298. [Google Scholar]
  25. Gibson RS. (2005b). Principles of nutritional assessment, 2nd ed New York, NY: Oxford University Press. [Google Scholar]
  26. Gierach GL, Geller BM, Shepherd JA, Patel DA, Vacek PM, Weaver DL, et al. (2014). Comparison of mammographic density assessed as volumes and areas among women undergoing diagnostic image-guided breast biopsy. Cancer Epidemiol Biomarkers Prev 23:2338–2348. [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Guthrie JR, Milne RL, Hopper JL, Cawson J, Dennerstein L, Burger HG. (2007). Mammographic densities during the menopausal transition: a longitudinal study of Australian-born women. Menopause 14:208–215. [DOI] [PubMed] [Google Scholar]
  28. Haars G, van Noord PA, van Gils CH, Grobbee DE, Peeters PH. (2005). Measurements of breast density: no ratio for a ratio. Cancer Epidemiol Biomarkers Prev 14 (Pt 1):2634–2640. [DOI] [PubMed] [Google Scholar]
  29. Harris HR, Tamimi RM, Willett WC, Hankinson SE, Michels KB. (2011). Body size across the life course, mammographic density, and risk of breast cancer. Am J Epidemiol 174:909–918. [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Heng D, Gao F, Jong R, Fishell E, Yaffe M, Martin L, et al. (2004). Risk factors for breast cancer associated with mammographic features in Singaporean Chinese women. Cancer Epidemiol Biomarkers Prev 13 (Pt 1):1751–1758. [PubMed] [Google Scholar]
  31. Heymsfield SB, Baumgartner RN, Allison DB, Ross R.Bray GA, Bouchard C, James WPT. (2004). Evaluation of total and regional adiposity. Handbook of obesity etiology and pathophysiology. New York, NY: Marcel Dekker; 33–79. [Google Scholar]
  32. Huo CW, Chew GL, Britt KL, Ingman WV, Henderson MA, Hopper JL, Thompson EW. (2014). Mammographic density – a review on the current understanding of its association with breast cancer. Breast Cancer Res Treat 144:479–502. [DOI] [PubMed] [Google Scholar]
  33. Irwin ML, Aiello EJ, McTiernan A, Bernstein L, Gilliland FD, Baumgartner RN, et al. (2007). Physical activity, body mass index, and mammographic density in postmenopausal breast cancer survivors. J Clin Oncol 25:1061–1066. [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Jeffreys M, Warren R, Highnam R, Davey Smith G. (2008). Breast cancer risk factors and a novel measure of volumetric breast density: cross-sectional study. Br J Cancer 98:210–216. [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Kato I, Beinart C, Bleich A, Su S, Kim M, Toniolo PG. (1995). A nested case–control study of mammographic patterns, breast volume, and breast cancer (New York City, NY, United States). Cancer Causes Control 6:431–438. [DOI] [PubMed] [Google Scholar]
  36. Kodama S, Horikawa C, Fujihara K, Heianza Y, Hirasawa R, Yachi Y, et al. (2012). Comparisons of the strength of associations with future type 2 diabetes risk among anthropometric obesity indicators, including waist-to-height ratio: a meta-analysis. Am J Epidemiol 176:959–969. [DOI] [PubMed] [Google Scholar]
  37. Kulendran M, Salhab M, Mokbel K. (2009). Oestrogen-synthesising enzymes and breast cancer. Anticancer Res 29:1095–1109. [PubMed] [Google Scholar]
  38. Lokate M, Kallenberg MG, Karssemeijer N, van den Bosch MA, Peeters PH, van Gils CH. (2010). Volumetric breast density from full-field digital mammograms and its association with breast cancer risk factors: a comparison with a threshold method. Cancer Epidemiol Biomarkers Prev 19:3096–3105. [DOI] [PubMed] [Google Scholar]
  39. Lokate M, Peeters PH, Peelen LM, Haars G, Veldhuis WB, van Gils CH. (2011). Mammographic density and breast cancer risk: the role of the fat surrounding the fibroglandular tissue. Breast Cancer Res 13:R103. [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Lukaski HC, Bolonchuk WW, Hall CB, Siders WA. (1986). Validation of tetrapolar bioelectrical impedance method to assess human body composition. J Appl Physiol (1985) 60:1327–1332. [DOI] [PubMed] [Google Scholar]
  41. Lumeng CN, Deyoung SM, Bodzin JL, Saltiel AR. (2007). Increased inflammatory properties of adipose tissue macrophages recruited during diet-induced obesity. Diabetes 56:16–23. [DOI] [PubMed] [Google Scholar]
  42. Maskarinec G, Meng L, Ursin G. (2001). Ethnic differences in mammographic densities. Int J Epidemiol 30:959–965. [DOI] [PubMed] [Google Scholar]
  43. Maskarinec G, Morimoto Y, Daida Y, Laidevant A, Malkov S, Shepherd JA, Novotny R. (2011). Comparison of breast density measured by dual energy X-ray absorptiometry with mammographic density among adult women in Hawaii. Cancer Epidemiol 35:188–193. [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. McCormack VA, dos Santos Silva I. (2006). Breast density and parenchymal patterns as markers of breast cancer risk: a meta-analysis. Cancer Epidemiol Biomarkers Prev 15:1159–1169. [DOI] [PubMed] [Google Scholar]
  45. McCormack VA, Highnam R, Perry N, dos Santos Silva I. (2007). Comparison of a new and existing method of mammographic density measurement: intramethod reliability and associations with known risk factors. Cancer Epidemiol Biomarkers Prev 16:1148–1154. [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. McCormack VA, Perry N, Vinnicombe SJ, Silva Idos S. (2008). Ethnic variations in mammographic density: a British multiethnic longitudinal study. Am J Epidemiol 168:412–421. [DOI] [PubMed] [Google Scholar]
  47. Nagata C, Matsubara T, Fujita H, Nagao Y, Shibuya C, Kashiki Y, Shimizu H. (2005). Mammographic density and the risk of breast cancer in Japanese women. Br J Cancer 92:2102–2106. [DOI] [PMC free article] [PubMed] [Google Scholar]
  48. Narvaez CJ, Matthews D, LaPorta E, Simmons KM, Beaudin S, Welsh J. (2014). The impact of vitamin D in breast cancer: genomics, pathways, metabolism. Front Physiol 5:213. [DOI] [PMC free article] [PubMed] [Google Scholar]
  49. Nayeem F, Ju H, Brunder DG, Nagamani M, Anderson KE, Khamapirad T, Lu LJW. (2014). Similarity of fibroglandular breast tissue content measured from magnetic resonance and mammographic images and by a mathematical algorithm. Int J Breast Cancer 2014:961679. [DOI] [PMC free article] [PubMed] [Google Scholar]
  50. Nazare JA, Smith J, Borel AL, Aschner P, Barter P, van Gaal L, et al. , INSPIRE ME IAA Investigators (2015). Usefulness of measuring both body mass index and waist circumference for the estimation of visceral adiposity and related cardiometabolic risk profile (from the INSPIRE ME IAA study). Am J Cardiol 115:307–315. [DOI] [PubMed] [Google Scholar]
  51. Olson JE, Sellers TA, Scott CG, Schueler BA, Brandt KR, Serie DJ, et al. (2012). The influence of mammogram acquisition on the mammographic density and breast cancer association in the Mayo Mammography Health Study cohort. Breast Cancer Res 14:R147. [DOI] [PMC free article] [PubMed] [Google Scholar]
  52. Park J, Morley TS, Kim M, Clegg DJ, Scherer PE. (2014). Obesity and cancer – mechanisms underlying tumour progression and recurrence. Nat Rev Endocrinol 10:455–465. [DOI] [PMC free article] [PubMed] [Google Scholar]
  53. Pérez-Hernández AI, Catalán V, Gómez-Ambrosi J, Rodríguez A, Frühbeck G. (2014). Mechanisms linking excess adiposity and carcinogenesis promotion. Front Endocrinol (Lausanne) 5:65. [DOI] [PMC free article] [PubMed] [Google Scholar]
  54. Pettersson A, Tamimi RM. (2012). Breast fat and breast cancer. Breast Cancer Res Treat 135:321–323. [DOI] [PMC free article] [PubMed] [Google Scholar]
  55. Pettersson A, Hankinson SE, Willett WC, Lagiou P, Trichopoulos D, Tamimi RM. (2011). Nondense mammographic area and risk of breast cancer. Breast Cancer Res 13:R100. [DOI] [PMC free article] [PubMed] [Google Scholar]
  56. Pettersson A, Graff RE, Ursin G, Santos Silva ID, McCormack V, Baqlietto L, et al. (2014). Mammographic density phenotypes and risk of breast cancer: a meta-analysis. J Natl Cancer Inst 106:dju078 doi: 10.1093/jnci/dju078. [DOI] [PMC free article] [PubMed] [Google Scholar]
  57. Pierobon M, Frankenfeld CL. (2013). Obesity as a risk factor for triple-negative breast cancers: a systematic review and meta-analysis. Breast Cancer Res Treat 137:307–314. [DOI] [PubMed] [Google Scholar]
  58. Pollán M, Lope V, Miranda-García J, García M, Casanova F, Sánchez-Contador C, et al. , DDM-Spain (2012). Adult weight gain, fat distribution and mammographic density in Spanish pre- and post-menopausal women (DDM-Spain). Breast Cancer Res Treat 134:823–838. [DOI] [PMC free article] [PubMed] [Google Scholar]
  59. Pouliot MC, Després JP, Lemieux S, Moorjani S, Bouchard C, Tremblay A, et al. (1994). Waist circumference and abdominal sagittal diameter: best simple anthropometric indexes of abdominal visceral adipose tissue accumulation and related cardiovascular risk in men and women. Am J Cardiol 73:460–468. [DOI] [PubMed] [Google Scholar]
  60. Preston-Martin S, Pike MC, Ross RK, Jones PA, Henderson BE. (1990). Increased cell division as a cause of human cancer. Cancer Res 50:7415–7421. [PubMed] [Google Scholar]
  61. Pull CB, Aguayo GA. (2011). Assessment of body-image perception and attitudes in obesity. Curr Opin Psychiatry 24:41–48. [DOI] [PubMed] [Google Scholar]
  62. Quetelet A. (1869). Physiquesociale: ou, Essai sur le développement des facultés de l’homme [A treatise on men and the development of his faculties]. Bruxelles, Belgium: C. Moquardt. [Google Scholar]
  63. Reeves KW, Stone RA, Modugno F, Ness RB, Vogel VG, Weissfeld JL, et al. (2009). Longitudinal association of anthropometry with mammographic breast density in the Study of Women’s Health Across the Nation. Int J Cancer 124:1169–1177. [DOI] [PMC free article] [PubMed] [Google Scholar]
  64. Russo J, Gusterson BA, Rogers AE, Russo IH, Wellings SR, van Zwieten MJ. (1990). Comparative study of human and rat mammary tumorigenesis. Lab Invest 62:244–278. [PubMed] [Google Scholar]
  65. Samimi G, Colditz GA, Baer HJ, Tamimi RM. (2008). Measures of energy balance and mammographic density in the Nurses’ Health Study. Breast Cancer Res Treat 109:113–122. [DOI] [PubMed] [Google Scholar]
  66. Santander AM, Lopez-Ocejo O, Casas O, Agostini T, Sanchez L, Lamas-Basulto E, et al. (2015). Paracrine interactions between adipocytes and tumor cells recruit and modify macrophages to the mammary tumor microenvironment: the role of obesity and inflammation in breast adipose tissue. Cancers (Basel) 7:143–178. [DOI] [PMC free article] [PubMed] [Google Scholar]
  67. Schetter SE, Hartman TJ, Liao J, Richie JP, Prokopczyk B, DuBrock C, et al. (2014). Differential impact of body mass index on absolute and percent breast density: implications regarding their use as breast cancer risk biomarkers. Breast Cancer Res Treat 146:355–363. [DOI] [PubMed] [Google Scholar]
  68. Schutz Y, Byrne NM, Dulloo A, Hills AP. (2014). Energy gap in the aetiology of body weight gain and obesity: a challenging concept with a complex evaluation and pitfalls. Obes Facts 7:15–25. [DOI] [PMC free article] [PubMed] [Google Scholar]
  69. Shepherd JA, Kerlikowske K. (2012). Do fatty breasts increase or decrease breast cancer risk? Breast Cancer Res 14:102. [DOI] [PMC free article] [PubMed] [Google Scholar]
  70. Shepherd JA, Kerlikowske K, Ma L, Duewer F, Fan B, Wang J, et al. (2011). Volume of mammographic density and risk of breast cancer. Cancer Epidemiol Biomarkers Prev 20:1473–1482. [DOI] [PMC free article] [PubMed] [Google Scholar]
  71. Siri WE.Brozek. J, Henschel A. (1961). Body composition from fluid spaces and density: analysis of methods. Techniques for measuring body composition. Washington, DC: National Academy of Sciences; 223–244. [Google Scholar]
  72. Soguel L, Diorio C. (2016). Anthropometric factors, adult weight gain, and mammographic features. Cancer Causes Control 27:333–340. [DOI] [PubMed] [Google Scholar]
  73. Sorensen TI. (2009). Conference on ‘Multidisciplinary approaches to nutritional problems’. Symposium on ‘Diabetes and health’. Challenges in the study of causation of obesity. Proc Nutr Soc 68:43–54. [DOI] [PubMed] [Google Scholar]
  74. Stone J, Warren RM, Pinney E, Warwick J, Cuzick J. (2009). Determinants of percentage and area measures of mammographic density. Am J Epidemiol 170:1571–1578. [DOI] [PubMed] [Google Scholar]
  75. Stone J, Ding J, Warren RM, Duffy SW, Hopper JL. (2010). Using mammographic density to predict breast cancer risk: dense area or percentage dense area. Breast Cancer Res 12:R97. [DOI] [PMC free article] [PubMed] [Google Scholar]
  76. Stunkard AJ, Sørensen T, Schulsinger F. (1983). Use of the Danish Adoption Register for the study of obesity and thinness. Res Publ Assoc Res Nerv Ment Dis 60:115–120. [PubMed] [Google Scholar]
  77. Sun Q, van Dam RM, Spiegelman D, Heymsfield SB, Willett WC, Hu FB. (2010). Comparison of dual-energy X-ray absorptiometric and anthropometric measures of adiposity in relation to adiposity-related biologic factors. Am J Epidemiol 172:1442–1454. [DOI] [PMC free article] [PubMed] [Google Scholar]
  78. Sung J, Song YM, Stone J, Lee K, Kim SY. (2010). Association of body size measurements and mammographic density in Korean women: the healthy twin study. Cancer Epidemiol Biomarkers Prev 19:1523–1531. [DOI] [PubMed] [Google Scholar]
  79. Tamimi RM, Hankinson SE, Colditz GA, Byrne C. (2005). Endogenous sex hormone levels and mammographic density among postmenopausal women. Cancer Epidemiol Biomarkers Prev 14 (Pt 1):2641–2647. [DOI] [PubMed] [Google Scholar]
  80. Tchernof A, Després JP. (2013). Pathophysiology of human visceral obesity: an update. Physiol Rev 93:359–404. [DOI] [PubMed] [Google Scholar]
  81. Tehranifar P, Reynolds D, Flom J, Fulton L, Liao Y, Kudadjie-Gyamfi E, Terry MB. (2011). Reproductive and menstrual factors and mammographic density in African American, Caribbean, and White women. Cancer Causes Control 22:599–610. [DOI] [PMC free article] [PubMed] [Google Scholar]
  82. Torres-Mejía G, de Stavola B, Allen DS, Pérez-Gavilán JJ, Ferreira JM, Fentiman IS, Dos Santos Silva I. (2005). Mammographic features and subsequent risk of breast cancer: a comparison of qualitative and quantitative evaluations in the Guernsey prospective studies. Cancer Epidemiol Biomarkers Prev 14:1052–1059. [DOI] [PubMed] [Google Scholar]
  83. Trichopoulos D, Lagiou P, Adami HO. (2005). Towards an integrated model for breast cancer etiology: the crucial role of the number of mammary tissue-specific stem cells. Breast Cancer Res 7:13–17. [DOI] [PMC free article] [PubMed] [Google Scholar]
  84. Tseng M, Byrne C. (2011). Adiposity, adult weight gain and mammographic breast density in US Chinese women. Int J Cancer 128:418–425. [DOI] [PMC free article] [PubMed] [Google Scholar]
  85. Ursin G, Sun CL, Koh WP, Khoo KS, Gao F, Wu AH, Yu MC. (2006). Associations between soy, diet, reproductive factors, and mammographic density in Singapore Chinese women. Nutr Cancer 56:128–135. [DOI] [PubMed] [Google Scholar]
  86. Vachon CM, Sasano H, Ghosh K, Brandt KR, Watson DA, Reynolds C, et al. (2011). Aromatase immunoreactivity is increased in mammographically dense regions of the breast. Breast Cancer Res Treat 125:243–252. [DOI] [PMC free article] [PubMed] [Google Scholar]
  87. Vongsuvanh R, George J, Qiao L, van der Poorten D. (2013). Visceral adiposity in gastrointestinal and hepatic carcinogenesis. Cancer Lett 330:1–10. [DOI] [PubMed] [Google Scholar]
  88. Wanders JO, Bakker MF, Veldhuis WB, Peeters PH, van Gils CH. (2015). The effect of weight change on changes in breast density measures over menopause in a breast cancer screening cohort. Breast Cancer Res 17:74. [DOI] [PMC free article] [PubMed] [Google Scholar]
  89. Wang YY, Lehuédé C, Laurent V, Dirat B, Dauvillier S, Bochet L, et al. (2012). Adipose tissue and breast epithelial cells: a dangerous dynamic duo in breast cancer. Cancer Lett 324:142–151. [DOI] [PubMed] [Google Scholar]
  90. Warren R, Thompson D, del Frate C, Cordell M, Highnam R, Tromans C, et al. (2007). A comparison of some anthropometric parameters between an Italian and a UK population: ‘proof of principle’ of a European project using MammoGrid. Clin Radiol 62:1052–1060. [DOI] [PubMed] [Google Scholar]
  91. WHO Consultation on Obesity (2000). Obesity: preventing and managing the global epidemic. Geneva, Switzerland: WHO technical report series. [PubMed] [Google Scholar]
  92. WHO Expert Commitee on Physical Status (1995). Physical status: the use and interpretation of anthropometry, report. Geneva, Switzerland: WHO technical report series. [PubMed] [Google Scholar]
  93. WHO Expert Consultation (2004). Appropriate body-mass index for Asian populations and its implications for policy and intervention strategies. Lancet 363:157–163. [DOI] [PubMed] [Google Scholar]
  94. Willett W. (2013). Nutritional epidemiology, 3rd ed Oxford, UK: Oxford University Press. [Google Scholar]
  95. Wolfe JN. (1976). Risk for breast cancer development determined by mammographic parenchymal pattern. Cancer 37:2486–2492. [DOI] [PubMed] [Google Scholar]
  96. Woolcott CG, Cook LS, Courneya KS, Boyd NF, Yaffe MJ, Terry T, et al. (2011). Associations of overall and abdominal adiposity with area and volumetric mammographic measures among postmenopausal women. Int J Cancer 129:440–448. [DOI] [PubMed] [Google Scholar]
  97. World Cancer Research Fund, American Institute for Cancer Research (2010). Continuous update project report. Food, nutrition, physical activity, and the prevention of breast cancer.
  98. Yaghjyan L, Colditz GA, Rosner B, Tamimi RM. (2013). Mammographic breast density and subsequent risk of breast cancer in postmenopausal women according to the time since the mammogram. Cancer Epidemiol Biomarkers Prev 22:1110–1117. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

SUPPLEMENTARY MATERIAL
cej-26-511-s001.pdf (137.5KB, pdf)
cej-26-511-s002.pdf (137KB, pdf)
cej-26-511-s003.pdf (182.3KB, pdf)

Supplemental digital content is available for this article. Direct URL citations appear in the printed text and are provided in the HTML and PDF versions of this article on the journal's website (www.eurjcancerprev.com).

Articles from European Journal of Cancer Prevention are provided here courtesy of Wolters Kluwer Health

RESOURCES