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Association between breastfeeding, mammographic density, and breast cancer risk: a review

International Breastfeeding Journal volume 19, Article number: 65 (2024) Cite this article

Abstract

Background

Mammographic density has been associated with breast cancer risk, and is modulated by established breast cancer risk factors, such as reproductive and hormonal history, as well as lifestyle. Recent epidemiological and biological findings underscore the recognized benefits of breastfeeding in reducing breast cancer risk, especially for aggressive subtypes. Current research exploring the association among mammographic density, breastfeeding, and breast cancer is sparse.

Main findings

Changes occur in the breasts during pregnancy in preparation for lactation, characterized by the proliferation of mammary gland tissues and the development of mammary alveoli. During lactation, the alveoli fill with milk, and subsequent weaning triggers the involution and remodeling of these tissues. Breastfeeding influences the breast microenvironment, potentially altering mammographic density. When breastfeeding is not initiated after birth, or is abruptly discontinued shortly after, the breast tissue undergoes forced and abrupt involution. Conversely, when breastfeeding is sustained over an extended period and concludes gradually, the breast tissue undergoes slow remodeling process known as gradual involution. Breast tissue undergoing abrupt involution displays denser stroma, altered collagen composition, heightened inflammation and proliferation, along with increased expression of estrogen receptor α (ERα) and progesterone receptor. Furthermore, elevated levels of pregnancy-associated plasma protein-A (PAPP-A) surpass those of its inhibitors during abrupt involution, enhancing insulin-like growth factor (IGF) signaling and collagen deposition. Prolactin and small molecules in breast milk may also modulate DNA methylation levels. Drawing insights from contemporary epidemiological and molecular biology studies, our review sheds light on how breastfeeding impacts mammographic density and explores its role in influencing breast cancer.

Conclusion

This review highlights a clear protective link between breastfeeding and reduced breast cancer risk via changes in mammographic density. Future research should investigate the effects of breastfeeding on mammographic density and breast cancer risk among various ethnic groups and elucidate the molecular mechanisms underlying these associations. Such comprehensive research will enhance our understanding and facilitate the development of targeted breast cancer prevention and treatment strategies.

Background

According to global cancer statistics, female breast cancer has emerged as the most diagnosed cancer worldwide, ranking as the foremost contributor to cancer incidence [1]. Several countries have implemented regular breast cancer screening programs, utilizing mammography to enhance the early detection of this disease. Over the past few decades, there has been more focus on identifying various risk factors associated with breast cancer, including specific gene mutations, mammographic density, reproductive factors, lifestyle choices, dietary habits, environmental exposures, and more [2] (Fig. 1). High mammographic density, among the risk factors associated with breast cancer, is notably recognized as an independent one that increases breast carcinogenesis [3,4,5] and which can diminish the sensitivity of mammography in screening procedures [6,7,8]. It also stands out for being non-static in the span of a woman’s life, owing to its changeable nature which can be influenced by established breast cancer risk factors, such as reproductive and hormonal history, as well as lifestyle [9,10,11,12].

In recent decades, breastfeeding has become a topic of increasing concern, drawing particular interest in its implications and optimal duration. Recent epidemiological and biological research has underscored the acknowledged benefits of breastfeeding in mitigating the risk of breast cancer, notably of its more aggressive subtypes [13,14,15]. Currently, research exploring the association among mammographic density, breastfeeding, and breast cancer is still relatively sparse. Our review aims to analyze and synthesize the existing epidemiological studies on the association among mammographic density, breastfeeding, and breast cancer. In addition, we endeavor to elucidate the multifaceted dynamics of how mammographic density, influenced by breastfeeding, impacts breast cancer risk. Furthermore, we seek to highlight gaps in current knowledge and suggest directions for future research, particularly in understanding the mechanisms underlying these associations.

Mammographic density and breast cancer risk

Mammographic density is defined as the proportion of fibroglandular tissue relative to fatty tissue within the breast. The fibroglandular tissue, comprised of “fibrous” (stromal) and “glandular” (lobular and ductal) components, which absorbs ionizing radiation, manifests as white areas, in mammography [6]. Hence, it is commonly referred to as “dense tissue”. Breast cancer typically originates from the ductal and glandular components of the breast, which appear white on mammography. Consequently, dense tissue, due to its similar radiographic appearance, can obscure these cancers, effectively masking them from detection.

Two-dimensional digital mammography (DM) and digital breast tomosynthesis (DBT) are most commonly used technologies. To standardize clinical interpretations of imaging, the American College of Radiology introduced the Breast Imaging Reporting and Data System (BI-RADS), which comes up with four distinct risk categories of mammographic density. In the 5th edition, it delineated mammographic density categories as [16]: (a) Almost entirely fatty; (b) Scattered areas of fibroglandular density; (c) Heterogeneously dense, potentially obscuring the detection of small masses; and (d) Extremely dense, which lowers the sensitivity of mammography (Fig. 2).

Numerous epidemiological studies have demonstrated that high mammographic density is associated with an increased risk of breast cancer, with this evidence primarily derived from cohort and case-control studies [3,4,5, 17,18,19,20]. Initial case-control studies conducted by Wolfe et al. [21] and Boyd et al. [22] revealed that women with the highest mammographic density category are more likely to develop breast cancer compared to those with low mammographic density category. This significant finding suggests that high mammographic density may serve as a surrogate marker of increased breast cancer risk. The association has been later confirmed by research conducted on various ethnic populations worldwide, under the guidance of a singular measurement of mammographic density, however, many studies assessing the correlation between mammographic density and the risk of breast cancer have relied on a singular measurement of mammographic density. This approach presents considerable variability in the interval between the diagnosis of breast cancer and the timing of the last negative mammogram [23].

Contribution of genetic factors to mammographic density

Given the substantial body of research establishing the heritable nature of mammographic density and its role as a significant risk factor for breast cancer [24,25,26], particularly among individuals with a family history, there’s compelling evidence for the genetic influence on mammographic density [18, 25, 27, 28]. For instance, a cohort study involving 1,370 women from 258 independent families pinpointed the influence of a major autosomal gene on mammographic density, as demonstrated by separation analysis [25]. Furthermore, classic twin studies have delineated the substantial genetic contribution to variations in mammographic density, revealing heritability estimates ranging from 60 to 75% [26]. The significant heritability of mammographic density underscores the necessity of research into the shared genetic loci that influence both mammographic density and breast cancer [29,30,31,32]. This necessitates a deeper exploration into the mechanistic pathways linking these genetic loci with breast cancer risk, offering a fertile ground for advancing our understanding and potentially uncovering new therapeutic targets.

In-depth whole-genome array analysis further revealed how specific genetic variations influence breast cancer risk by modulating mammographic density. For instance, the down-regulation of UGT genes due to exposure to female sex hormones is directly associated with high mammographic density, which could further increase the risk of breast cancer [33]. This discovery not only enriches the comprehension of the hormonal-genetic interplay affecting mammographic density but also prompts further investigation into the preventive strategies that could mitigate these risks. Further study has discovered that tumors in different mammographic densities exhibit distinct genetic characteristics, such as an increased frequency of TP53 mutations and the extent of genomic alterations, revealing how mammographic density is linked to breast cancer risk through genetic pathways [34]. Furthermore, research has shown that single-nucleotide polymorphisms (SNPs) are associated with mammographic density, indicating that these genetic variations may influence breast cancer risk by altering mammographic density [35, 36]. A meta-analysis [37] of 68 studies identified key genetic modifiers for mammographic density such as ESR1 and IGF1, suggesting their potential as biomarkers for risk assessment. Additionally, a study linking polygenic risk scores (PRS) with mammographic density underscores the genetic basis of breast cancer risk [24]. These findings underscore the importance of further exploring the genetic basis of mammographic density for the early identification of breast cancer risk and the development of personalized screening strategies.

Contribution of environmental factors to mammographic density

Mammographic density is known to be influenced by reproductive history, lifestyle choices, and exogenous hormones [10, 38,39,40,41], indicating that it may serve as a marker of exposure to environmental factors that predispose women to breast cancer. Pike et al. [42] has proposed a model of breast cancer incidence that shifts focus from chronological age to the concept of “breast tissue exposure”. This model identifies the exposure of breast tissue to hormonal and reproductive factors as critical determinants, affecting the tissue’s susceptibility to carcinogens. Generally, mammographic density either remains stable or decreases incrementally with time. An increase in mammographic density, however, may signify proliferative changes that surpass the effects of aging. Changes in mammographic density can reflect the hormonal environment of a woman, as well as the reproductive factors that are associated with breast cancer risk.

Puberty is a critical period for breast development, during which endocrine and paracrine factors stimulate the growth of epithelial, stromal, and adipose tissues in the breast [43, 44]. The relative proportions of these different cell types determine mammographic density and influence the risk of breast cancer. Epidemiological studies have found that timing of puberty, onset of pubertal breast development, onset of menstrual cycling, and pubertal tempo, were associated with adult mammographic density [45,46,47,48,49,50]. For example, Schoemaker et al. [46] found that earlier pubertal onset was associated with lower adult mammographic density. Another prospective cohort study found that a slower pubertal tempo was associated with higher mammographic density in young women [45]. Some studies have found that later age at menarche was positively associated with mammographic density [47,48,49,50], while others found no relationship between age at menarche and mammographic density [11, 51, 52]. Additionally, fatty deposition and glandular involution post-menopause can significantly influence changes in mammographic density [53,54,55]. Many epidemiological studies have used subgroup analyses, stratifying populations by menopausal status to adjust for potential confounding factors.

Some studies have explored the inverse relationship between body mass index (BMI) and mammographic density, indicating that higher BMI is often associated with lower mammographic density [11, 56,57,58,59,60]. This trend persists when examining the impact of pubertal adiposity, as measured by BMI, on mammographic density in adult women [47, 48, 61]. Epidemiological studies have also evaluated the association between mammographic density and other risk factors, such as alcohol consumption, smoking, diet, and physical exercise; however, the results were not always consistent [62,63,64,65,66,67]. The variability in findings across different studies highlights the intricate interplay of genetic and environmental factors that contribute to mammographic density and, by extension, breast cancer risk.

Breast tissue composition associated with mammographic density

Breast tissue comprises mammary epithelial cells, where breast cancer mostly originates, as well as stroma, including mammary fibroblasts, collagen, immune cells, and the extracellular matrix (ECM), and adipose tissue [68, 69]. The relative abundance of these components determines mammographic density that appears on the mammography. The intricate interplay between the epithelium, stroma, and ECM may play a role in elevating mammographic density and, consequently, breast cancer risk.

Epithelium and stroma

Some studies, utilizing surgical biopsies or mastectomy specimens for histological comparison with mammographically determined mammographic density, have found that increased amounts of epithelium and/or stroma are associated with higher mammographic density [70,71,72,73,74]. The structural and signaling scaffold provided by the stroma not only encapsulates epithelial structures but also fosters their optimal development and functionality, mediating this through paracrine growth factors [75]. Increased stromal density directly regulates the three-dimensional mechanical microenvironment of mammary epithelial cells, influencing proliferation and phenotype [76]. However, not all studies found an increase in cell proliferation in denser breast tissues, indicating that other factors might be at play [74, 77, 78]. In a study analyzing samples from women undergoing prophylactic mastectomy, tissues with high mammographic density exhibited increased collagen deposition and organization compared to those with low mammographic density [74]. Additionally, another study confirmed that high mammographic density is associated with a higher proportion of stromal composition, particularly increased collagen density and fibrosis extent [79]. These findings underscore the role of the breast’s extracellular matrix in modulating tissue stiffness and potentially facilitating a pro-carcinogenic microenvironment. Interactions within the breast microenvironment influence each component, and changes induced by various factors in any component can directly or indirectly alter the microenvironment, thereby influencing the risk of breast cancer.

Fibroblasts

Fibroblasts, the predominant cell type in the mammary stroma, play a crucial role in the production and turnover regulation of collagen and other ECM components [69]. In regions of high mammographic density, fibroblasts may secrete soluble factors that trigger the proliferation of epithelial cells [75, 80]. Subsequently, these overstimulated epithelial cells may experience phenotypic alterations and release factors that re-activate fibroblasts, creating a dynamic feedback loop that further stimulates fibroblast activity [75]. Continuous activation of fibroblasts can lead to the excessive accumulation of these components, resulting in fibrosis [69]. This process can be modulated by signals from immune cells present in the mammary stroma [34, 81]. Research has consistently shown that, compared to low mammographic density, high mammographic density is associated with a more pro-inflammatory environment [34, 81, 82]. The chronic pro-inflammatory milieu in dense breast tissue could serve as a priming ground for oncogenic mutations, facilitating the transition from hyperplasia to malignancy.

Collagen

Collagen, a key fibrous protein within the ECM, is now recognized not just for its structural role but also for its dynamic involvement in breast cancer pathogenesis. The established correlation between collagen’s abundance and alignment in tissues with high mammographic density underscores the intricate relationship between the ECM composition and tumorigenesis [83]. Provenzano et al. [76] utilized a bi-transgenic tumor model featuring increased stromal collagen in mouse mammary tissue to assess the impact of collagen density. Their findings revealed that heightened collagen levels in mouse mammary tissue significantly accelerated tumor formation and led to a markedly more invasive phenotype. Further analysis utilizing second harmonic generation imaging on breast biopsies, discovered that as the severity of diagnosis increased, collagen fibers tended to be less dense, shorter, straighter, thinner, and more aligned with each other [83]. The increased deposition of fibrillar collagen has been demonstrated to lead to enhanced matrix stiffness and disrupt physiological mammary morphogenesis [84].

Targeting the mechanical properties of the ECM may offer new avenues for cancer treatment. A study provided direct 3D experimental evidence showing that ECM alignment and density could expedite breast cancer progression by fostering fibroblast induction and inhibiting T-cell activation [85]. This insight of the ECM’s capacity in modulating the immune response highlights an underexplored avenue for the potential treatment role of ECM organization and density in breast cancer. Lysyl oxidase (LOX), a crucial mediator of collagen crosslinking, emerges as a potential therapeutic target due to its significant contribution to stromal stiffening and its pivotal role in maintaining ECM integrity [86]. The activity of LOX is essential for reinforcing the tensile strength of the ECM, which suggests that modulating LOX activity could alter the tumor microenvironment and potentially inhibit tumor progression [87].

Within this dynamic mechanical microenvironment, epithelial cells are significantly influenced by the dense collagenous stroma. At the molecular level, the identification of key pathways regulating epithelial-stromal interactions, particularly those mediated by focal adhesion kinase (FAK), opens up new possibilities for targeting cell adhesion and migration in cancer therapy [88]. FAK, a widely expressed non-receptor protein tyrosine kinase, is pivotal in regulating cell proliferation and gene expression via Extracellular-Signal Regulated Kinase (ERK)-mediated pathways [88]. It initiates cytoskeletal rearrangements and cellular shape changes, promoting actomyosin stress fiber formation [89]. Additionally, FAK influences cell motility and migration by loosening focal adhesions and upregulating MMP-9, contributing to the invasive phenotype of breast cancer [90]. In conclusion, while these findings advance our understanding of collagen’s role in breast cancer, they also beckon further research into how these mechanisms can be harnessed for clinical benefit. Future studies should aim to explore the translational potential of targeting ECM components and their regulatory pathways, such as LOX and FAK, to develop novel therapeutic strategies against breast cancer.

Other ECM components

The ECM is a complex and dynamic entity that, besides including collagen, encompasses other proteins such as laminin, fibronectin, proteoglycans (PGs), and proteases [91]. These components act as a structural scaffold, offering support for tissue assembly, maintenance, and integrity. Small leucine-rich proteoglycans (SLRPs), such as lumican, decorin, fibromodulin, and biglycan, play a crucial role as components of the ECM and are associated with increased tissue density [92,93,94]. Additionally, the accumulation of the matrix proteoglycan versican within the tumor stroma has been correlated with dense breast tissue and malignant transformation [95].

Immune system components

Several studies have showed that dense breast tissue was associated with a pro-inflammatory microenvironment that may promote cancer development [96,97,98]. Various immune cells, including macrophages, eosinophils, neutrophils, and lymphocytes (T and B cells), are integral to the mammary gland’s development and transformation [99]. These cells contribute to inflammatory responses and shape the tumor microenvironment by secreting cytokines, enzymes, and chemokines (Table 1) [100,101,102,103].

Table 1 The function of various immune cells in breast microenvironment
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There still remain many gaps in our understanding of the cellular and molecular mechanisms that underpin the strong association between mammographic density and breast cancer predisposition. The evidence points to a complex network of interactions that govern the relationship between mammographic density and cancer risk, implying that risk reduction strategies must account for the intricate composition of breast tissue and its microenvironment.

Breastfeeding and breast cancer risk

Breast tissue undergoes continuous transformation throughout an individual’s lifetime. This includes the expansion and development of the mammary gland during puberty, cyclic proliferation and involution corresponding with menstrual cycles, glandular and ductal adaptations during lactation, and the deposition of fatty tissues coupled with further involution post-menopause [10]. Recent epidemiological findings underscore the recognized benefits of breastfeeding in reducing breast cancer risk, especially for aggressive subtypes [14, 104,105,106,107,108,109]. A collaborative reanalysis involving 47 epidemiological studies across 30 countries has demonstrated that the relative risk of breast cancer decreases by 4.3% for each year of breastfeeding [14]. This relationship holds true consistently across women from both developed and developing countries, spanning various ages and ethnic origins, and encompassing diverse childbearing patterns and personal characteristics. Specifically, breastfeeding has been shown to reduce the risk of triple-negative breast cancer (TNBC) by 20% and offers a risk reduction of 22–50% in carriers of BRCA1 mutations [107, 110,111,112]. Notably, a study including 4,000 Black women diagnosed with breast cancer, alongside 14,000 control participants, revealed a correlation between childbearing and an elevated risk of estrogen receptor negative (ER-) and TNBC. Crucially, this study also identified breastfeeding as a significant mitigating factor in reducing this heightened risk [113].

Childbearing is generally linked to a long-term reduction in breast cancer risk; however, recent research indicates a nuanced pattern, particularly noting a transient increase in risk during the early postpartum period, specifically within the first 5–10 years following childbirth [114,115,116]. Key factors influencing this increased risk include maternal age at first childbirth, with older first-time mothers facing higher risks compared to younger ones [116], and the duration of breastfeeding [14, 117]. Extended breastfeeding, particularly beyond 12 months, is associated with substantial protective benefits against breast cancer [104, 105, 118]. This protective effect underscores the critical importance of breastfeeding duration in mitigating breast cancer risk. Additionally, women with no prior births (nulliparity) and those who delay childbearing are often found to have higher mammographic densities, further elevating their breast cancer risk [55, 115, 119]. The challenge is compounded by the lower frequency of mammographic screenings in young postpartum women, resulting in cancers that are typically self-detected, larger, and more advanced than those identified through routine screenings. To effectively manage and potentially mitigate these risks, it is crucial to adapt breast cancer screening strategies to accommodate the unique circumstances of young mothers, particularly during the vulnerable postpartum period. This adaptation may involve reassessing the timing and frequency of mammograms to ensure early detection and treatment of breast cancer in this high-risk group.

Overall, based on the epidemiological studies, breastfeeding exerts a protective effect against breast cancer, offering significant benefits for public health. Several mechanisms are proposed to explain how this effect comes about [120, 121]: Firstly, breastfeeding promotes differentiation of breast cells for milk production, potentially reducing their vulnerability to carcinogenic transformation. Secondly, breastfeeding is associated with ovulatory cycles. The decrease in ovulation reduces the exposure to estrogen and other hormones that can promote breast cancer growth. Thirdly, during lactation, the shedding of breast epithelium may help to remove mutated cells and potentially eliminate carcinogens, thereby reducing the risk of cancer development. The concurrence of all these mechanisms represents the complex biological pathways through which lactation may confer protection against breast cancer and emphasize its role in cancer prevention strategies. It is thus imperative that breastfeeding should be promoted as a potentially impactful strategy in reducing breast cancer incidence.

Breastfeeding and mammographic density

The significant effect of breastfeeding and mammographic density on women’s health, can be better understood in their relevance to breast cancer risk. Breastfeeding acts as a protective factor, whereas dense breast tissue is considered a risk factor. However, research on the association between the two has yielded inconsistent results, owing to the complex relationship between them and breast cancer risk which is liable to a combination of genetic, hormonal, and environmental factors. Furthermore, the underlying biological mechanisms whereby breastfeeding may influence mammographic density remain uncertain.

Epidemiological evidence

The epidemiological studies on the association between breastfeeding and mammographic density have led to inconsistent findings [11, 38, 54, 57, 122,123,124,125,126,127,128]. Some link breastfeeding with lower mammographic density [122, 125, 126, 128], particularly with prolonged duration, some suggest the opposite, associating it with higher mammographic density [11, 54], while others negate any significant relationship between the two [57, 124, 127]. These divergent conclusions can be attributed to an assortment of variables, one of them being menopausal status, that hinder a clear understanding of this association [38, 125, 128]. Another variable is the duration of breastfeeding which differs according to educational background and socioeconomic status. Reproductive history may also affect the results derived from related epidemiological studies by way of the number of pregnancies, the intervals between pregnancies, the duration of breastfeeding following each pregnancy, the duration of exclusive breastfeeding. All in all, the diversity of breastfeeding practices and reproductive patterns justifies the complexity of the problem but without showing the way out.

Biological mechanisms

The biological mechanisms through which breastfeeding impacts mammographic density remain elusive. The pregnancy-lactation-involution cycle represents a dynamic and multi-step process susceptible to many factors and involves significant changes in the breast tissue, starting with pregnancy, continuing through lactation, and concluding with the post-lactation involution. Each stage of this cycle could potentially affect breast tissue composition and consequently mammographic density. Breastfeeding significantly affects the physiological structure of the mammary glands, involving the proliferation of mammary gland tissues and the development of mammary alveoli during pregnancy, the filling of alveoli and milk secretion during lactation, and the involution of alveoli and remodeling of mammary tissues during weaning [121]. Moreover, the breast microenvironment—encompassing gene expression, protein regulation, and the components of the ECM—may also be influenced by breastfeeding. All this evidence indicates that the varied epidemiological findings on the association between breastfeeding and mammographic density could be a reflection of the complex mechanisms that regulate mammary gland development, as well as the multifaceted determinants of mammographic density. The biological impact of lactation on mammographic density is summarized in Fig. 3.

Breastfeeding and involution

The post-lactational involution of breast tissues involve unique changes in the microenvironment, distinctly different from those that have not undergone lactation [115]. During mammary gland involution, a significant portion of mammary epithelial cells undergo programmed cell death, while others return to their pre-pregnancy state [120]. This remodeling process is influenced by the duration of breastfeeding, suggesting that longer periods of lactation may lead to more pronounced changes in the breast tissue’s structure and composition. These alterations could potentially impact the overall breast microenvironment. When breastfeeding is not initiated after birth or is abruptly discontinued shortly after, the breast tissue undergoes a forced and abrupt involution. Conversely, when breastfeeding is sustained over an extended period and concludes gradually, the breast tissue undergoes a slow remodeling process known as gradual involution. Considering the clinical significance of understanding breast tissue dynamics, particularly in the context of breast cancer risk, it becomes imperative to delve into the implications of these distinct involution patterns. By elucidating the differences between abrupt and gradual involution and their consequences for breast tissue composition and microenvironment, we can potentially uncover novel avenues for risk assessment and prevention strategies. It is shown that under abrupt involution mammary glands from mice exhibited characteristics distinct from those undergoing gradual involution [129]. Specifically, they displayed denser stroma, altered collagen composition, heightened inflammation and proliferation, along with increased expression of estrogen receptor α (ERα) and progesterone receptor, compared to gradual involution. Furthermore, abrupt involution led to significant ductal hyperplasia, squamous metaplasia, and a sustained increase in luminal progenitor cells. These findings highlight the profound impact of the involution process on breast microenvironment, suggesting potential implications for understanding breast tissue changes and cancer risk associated with different breastfeeding patterns.

Breastfeeding and breast microenvironment

Breastfeeding significantly impacts the breast microenvironment, influencing the behavior and characteristics of epithelial cells and the ECM. In scenarios where breastfeeding is not initiated or is of short duration, a terminal bud structure persists in the breast tissue following involution [130]. This structural remnant leaves epithelial cells more susceptible to carcinogenic stimulation, potentially facilitating the transformation of these cells into cancerous ones [130]. Abrupt involution in mouse mammary glands has been shown to correlate with increased collagen deposition and a higher ratio of type I to type III collagen [129]. This finding suggests that the duration of breastfeeding following each pregnancy could influence mammographic density. Moreover, abrupt involution is associated with heightened expression of ERα, increased proliferation index, and the presence of hyperplasia and squamous metaplasia [129]. The findings further revealed that breast tissue from healthy women who breastfed for less than 6 months exhibited a positive enrichment of genes within the Notch signaling pathways which indicated an active regulatory mechanism in these women’s breast tissue, which may influence breast development. It was demonstrated that constitutive Notch signaling specifically targets luminal progenitor cells for expansion [131]. This continuous activation can lead to hyperplasia, an abnormal increase in the number of cells, and potentially to tumorigenesis, the formation of tumors. This suggests that while Notch signaling is crucial for normal breast development, its dysregulation may contribute to breast cancer pathogenesis. This observation provides additional evidence suggesting that never breastfeeding or brief periods of breastfeeding might promote the expansion of luminal progenitor cells, which are associated with breast cancer. These changes indicate that never breastfeeding or short-term breastfeeding may contribute to the development of a pro-tumorigenic environment.

Extended lactation has been demonstrated to offer protection against carcinogenesis mediated by pregnancy-associated plasma protein-A (PAPP-A), a pregnancy-dependent oncogene [132]. Research involving transgenic mice with PAPP-A expression in the mammary gland during pregnancy and involution demonstrated that PAPP-A promotes collagen deposition [132]. This increase in collagen facilitates the proteolytic activity of PAPP-A on insulin-like growth factor-binding proteins 4 and 5 (IGFBP-4 and IGFBP-5), enhancing insulin-like growth factor (IGF) signaling and further collagen deposition. Notably, PAPP-A transgenic mice that lactated for extended periods did not develop mammary tumors, whereas those that lactated for shorter durations exhibited mammary tumors characterized by a specific collagen signature associated with tumors (TACS-3). The lactation-induced protective effect was linked to the upregulation of PAPP-A inhibitors, stanniocalcin-1 (STC1), and stanniocalcin-2 (STC2), with prolonged lactation correlating with higher levels of these inhibitors [132]. These elevated levels of STC1 and STC2 during extended lactation inactivate PAPP-A, even when overexpressed, preventing IGFBP-5 cleavage and allowing normal involution to proceed. Conversely, in the absence of lactation or during short lactation periods, PAPP-A levels surpass those of its inhibitors, STC1 and STC2, leading to excessive IGFBP-5 degradation. This imbalance establishes a positive feedback loop between increased collagen deposition and enhanced IGF signaling. This augmented collagen deposition intensifies mammographic density, which is a recognized risk factor for tumorigenesis. Thus, the altered microenvironment not only contributes to higher mammographic density but also directly influences the progression towards breast cancer.

Breastfeeding and DNA methylation

DNA methylation, a common epigenetic modification in mammals, is facilitated by DNA methyltransferase enzymes, such as DNMT1, DNMT3a, and DNMT3b [133]. DNA methylation plays an important role in modulating gene expression without changing the underlying DNA sequence. This modification is pivotal in regulating gene expression, enabling the silencing or activation of genes without altering the DNA sequence itself. Importantly, DNA methylation has been identified as a key factor in the tumorigenesis of breast cancer, suggesting that alterations in methylation patterns can contribute to the initiation and progression of cancers [134]. Recent research has revealed distinct DNA methylation patterns between women with high mammographic density and those with low mammographic density who later develop breast cancer [135]. Specifically, differences were noted in genes responsible for regulating DNA transcription and cell apoptosis. This finding suggests that variations in DNA methylation related to mammographic density may influence key biological pathways, potentially affecting the risk of developing breast cancer. The protective effect of breastfeeding against breast cancer is partly attributed to the stimulation of prolactin, which enhances the expression of DNA methyltransferase enzymes in mammary epithelial cells during lactation [136, 137]. This hormonal influence facilitates epigenetic modifications that could play a role in reducing cancer risk. Additionally, the presence of small molecules in breast milk, such as miR-29s, has been investigated for their role in regulating DNA methylation levels. MiR-29s specifically targets and inversely regulates DNMT3a and DNMT3b in mammary epithelial cells [138], suggesting a mechanism by which breastfeeding may influence the epigenetic landscape of the breast tissue. These findings highlight the complex interplay between hormonal changes during lactation and molecular mechanisms in breast milk that contribute to the protective effects of breastfeeding on breast cancer risk. Despite the recognition of the potential mechanisms by which breastfeeding influences breast cancer risk through DNA methylation, there have been limited studies directly evaluating the relationship between DNA methylation, breastfeeding, and mammographic density. This gap in research highlights the need for further investigation into how breastfeeding may impact mammographic density through epigenetic modifications such as DNA methylation. Understanding this relationship could provide valuable insights into the complex interplay between genetic and environmental factors in breast cancer risk and the protective role of breastfeeding.

Future directions

Mammographic density is a key indicator of the breast tissue microenvironment, subject to modulation by several established breast cancer risk factors. As a protective factor against breast cancer, prolonged lactation has been shown to reduce the risk of developing the disease. Drawing insights from contemporary epidemiological and molecular biology studies, our review sheds light on how lactation impacts on the mammographic density and explores their role in influencing the development of breast cancer (Fig. 4).

To address the complex relationship between breastfeeding, mammographic density, and breast cancer, a multi-faceted research strategy is paramount. Firstly, future research should focus on combining advanced imaging techniques and genetic analysis to better understand how breastfeeding affects mammographic density and breast cancer risk. This entails not only the refinement of imaging methodologies to accurately quantify changes in mammographic density but also the exploration of genetic markers that may influence these changes and their correlation with cancer development. Secondly, long-term studies are essential, especially those that consider different ethnic groups and extend over long periods. These studies should carefully record details about reproduction, such as the number of pregnancies, time between pregnancies, how long breastfeeding lasted after each pregnancy, and the duration of exclusive breastfeeding. This approach aims to clearly understand the connection between breastfeeding, changes in mammographic density, and breast cancer development over time among various populations. Thirdly, it is essential to probe into the interconnections between breastfeeding, mammographic density, lifestyle and environmental determinants. A holistic integration of these factors could unveil their cumulative influence on breast cancer risk, paving the way for identifying synergistic avenues for risk mitigation. Lastly, deepening our understanding of the association between breastfeeding, mammographic density, and breast cancer, developing innovative preventive and screening methodologies are helpful for breast cancer screening. Such strategies, particularly advantageous for individuals at elevated risk, would integrate insights into mammographic density and breastfeeding history into predictive models and screening protocols. Customizing screening timetables and methodologies to reflect individual risk profiles holds the potential to markedly amplify the efficacy of early detection.

Fig. 1
figure 1

The risk factors associated with breast cancer. Note: The figure was drawn by using Figdraw

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Fig. 2
figure 2

The images represent different categories of mammographic density: (A) almost entirely fatty; (B) scattered areas of fibroglandular density; (C) heterogeneously dense; (D) extremely dense. Source: Images courtesy of Cancer Hospital of Dalian University of Technology, Liaoning Cancer Hospital & Institute

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Fig. 3
figure 3

The biological impact of lactation on mammographic density

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Fig. 4
figure 4

The association between lactation, breast density and breast cancer

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Data availability

No datasets were generated or analysed during the current study.

References

  1. Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. Global Cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: A Cancer. J Clin. 2021;71(3):209–49.

    Google Scholar 

  2. Terry MB, Colditz GA. Epidemiology and risk factors for breast cancer: 21st century advances, gaps to address through interdisciplinary science. Cold Spring Harbor Perspect Med. 2023;13(9):a041317.

    Article  CAS  Google Scholar 

  3. Bodewes FTH, van Asselt AA, Dorrius MD, Greuter MJW, de Bock GH. Mammographic breast density and the risk of breast cancer: a systematic review and meta-analysis. Breast. 2022;66:62–8.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  4. Lynge E, Vejborg I, Lillholm M, Nielsen M, Napolitano G, von Euler-Chelpin M. Breast density and risk of breast cancer. Int J Cancer. 2023;152(6):1150–8.

    Article  PubMed  CAS  Google Scholar 

  5. Mann RM, Athanasiou A, Baltzer PAT, Camps-Herrero J, Clauser P, Fallenberg EM, et al. Breast cancer screening in women with extremely dense breasts recommendations of the European Society of Breast Imaging (EUSOBI). Eur Radiol. 2022;32(6):4036–45.

    Article  PubMed  PubMed Central  Google Scholar 

  6. Acciavatti RJ, Lee SH, Reig B, Moy L, Conant EF, Kontos D, et al. Beyond breast density: risk measures for breast cancer in multiple imaging modalities. Radiology. 2023;306(3):e222575.

    Article  PubMed  Google Scholar 

  7. Farkas AH, Nattinger AB. Breast cancer screening and prevention. Ann Intern Med. 2023;176(11):ITC161–76.

    Article  PubMed  Google Scholar 

  8. Hussein H, Abbas E, Keshavarzi S, Fazelzad R, Bukhanov K, Kulkarni S, et al. Supplemental breast cancer screening in women with dense breasts and negative mammography: a systematic review and Meta-analysis. Radiology. 2023;306(3):e221785.

    Article  PubMed  Google Scholar 

  9. Atakpa EC, Thorat MA, Cuzick J, Brentnall AR. Mammographic density, endocrine therapy and breast cancer risk: a prognostic and predictive biomarker review. Cochrane Database Syst Rev. 2021;10:CD013091.

    PubMed  Google Scholar 

  10. Lester SP, Kaur AS, Vegunta S. Association between lifestyle changes, mammographic breast density, and breast cancer. Oncologist. 2022;27(7):548–54.

    Article  PubMed  PubMed Central  Google Scholar 

  11. Rice MS, Bertrand KA, Lajous M, Tamimi RM, Torres G, López-Ridaura R, et al. Reproductive and lifestyle risk factors and mammographic density in Mexican women. Ann Epidemiol. 2015;25(11):868–73.

    Article  PubMed  PubMed Central  Google Scholar 

  12. Chen X, Wang M, Yu K, Xu S, Qiu P, Lyu Z, et al. Chronic stress-induced immune dysregulation in breast cancer: implications of psychosocial factors. J Translational Intern Med. 2023;11(3):226–33.

    Article  Google Scholar 

  13. Victora CG, Bahl R, Barros AJ, França GV, Horton S, Krasevec J, et al. Breastfeeding in the 21st century: epidemiology, mechanisms, and lifelong effect. Lancet. 2016;387(10017):475–90.

    Article  PubMed  Google Scholar 

  14. Breast cancer and. Breastfeeding: collaborative reanalysis of individual data from 47 epidemiological studies in 30 countries, including 50302 women with breast cancer and 96973 women without the disease. Lancet. 2002;360(9328):187–95.

    Article  Google Scholar 

  15. Kwan ML, Bernard PS, Kroenke CH, Factor RE, Habel LA, Weltzien EK, et al. Breastfeeding, PAM50 tumor subtype, and breast cancer prognosis and survival. J Natl Cancer Inst. 2015;107(7):djv087.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Sickles EA, D’Orsi CJ, Bassett LW, Appleton CM, Berg WA, Burnside ES, et al. ACR BI-RADS® Mammography. ACR BI-RADS® Atlas, Breast Imaging Reporting and Data System. Reston, VA: American College of Radiology; 2013.

    Google Scholar 

  17. Kim S, Tran TXM, Song H, Ryu S, Chang Y, Park B. Mammographic breast density, benign breast disease, and subsequent breast cancer risk in 3.9 million Korean women. Radiology. 2022;304(3):534–41.

    Article  PubMed  Google Scholar 

  18. Han Y, Moore JX, Colditz GA, Toriola AT. Family history of breast cancer and mammographic breast density in premenopausal women. JAMA Netw Open. 2022;5(2):e2148983.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Román M, Louro J, Posso M, Alcántara R, Peñalva L, Sala M, et al. Breast density, benign breast disease, and risk of breast cancer over time. Eur Radiol. 2021;31(7):4839–47.

    Article  PubMed  Google Scholar 

  20. Ye DM, Li Q, Yu T, Wang HT, Luo YH, Li WQ. Clinical and epidemiologic factors associated with breast cancer and its subtypes among Northeast Chinese women. Cancer Med. 2019;8(17):7431–45.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Wolfe JN, Saftlas AF, Salane M. Mammographic parenchymal patterns and quantitative evaluation of mammographic densities: a case-control study. Am J Roentgenol. 1987;148(6):1087–92.

    Article  CAS  Google Scholar 

  22. Boyd NF, Byng JW, Jong RA, Fishell EK, Little LE, Miller AB, et al. Quantitative classification of mammographic densities and breast cancer risk: results from the Canadian national breast screening study. J Natl Cancer Inst. 1995;87(9):670–5.

    Article  PubMed  CAS  Google Scholar 

  23. McCormack VA, dos Santos Silva I. Breast density and parenchymal patterns as markers of breast cancer risk: a meta-analysis. Cancer Epidemiol Biomarkers Prev. 2006;15(6):1159–69.

    Article  PubMed  Google Scholar 

  24. Holowko N, Eriksson M, Kuja-Halkola R, Azam S, He W, Hall P, et al. Heritability of mammographic breast density, density change, microcalcifications, and masses. Cancer Res. 2020;80(7):1590–600.

    Article  PubMed  CAS  Google Scholar 

  25. Pankow JS, Vachon CM, Kuni CC, King RA, Arnett DK, Grabrick DM, et al. Genetic analysis of mammographic breast density in adult women: evidence of a gene effect. J Natl Cancer Inst. 1997;89(8):549–56.

    Article  PubMed  CAS  Google Scholar 

  26. Boyd NF, Dite GS, Stone J, Gunasekara A, English DR, McCredie MR, et al. Heritability of mammographic density, a risk factor for breast cancer. N Engl J Med. 2002;347(12):886–94.

    Article  PubMed  Google Scholar 

  27. Maskarinec G, Nakamura KL, Woolcott CG, Conroy SM, Byrne C, Nagata C, et al. Mammographic density and breast cancer risk by family history in women of white and Asian ancestry. Cancer Causes Control. 2015;26(4):621–6.

    Article  PubMed  PubMed Central  Google Scholar 

  28. Ziv E, Shepherd J, Smith-Bindman R, Kerlikowske K. Mammographic breast density and family history of breast cancer. J Natl Cancer Inst. 2003;95(7):556–8.

    Article  PubMed  Google Scholar 

  29. Vachon CM, Scott CG, Fasching PA, Hall P, Tamimi RM, Li J, et al. Common breast cancer susceptibility variants in LSP1 and RAD51L1 are associated with mammographic density measures that predict breast cancer risk. Cancer Epidemiol Biomarkers Prev. 2012;21(7):1156–66.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  30. Odefrey F, Stone J, Gurrin LC, Byrnes GB, Apicella C, Dite GS, et al. Common genetic variants associated with breast cancer and mammographic density measures that predict disease. Cancer Res. 2010;70(4):1449–58.

    Article  PubMed  CAS  Google Scholar 

  31. Stone J, Thompson DJ, Dos Santos Silva I, Scott C, Tamimi RM, Lindstrom S, et al. Novel associations between common breast cancer susceptibility variants and risk-predicting mammographic density measures. Cancer Res. 2015;75(12):2457–67.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  32. Brand JS, Humphreys K, Thompson DJ, Li J, Eriksson M, Hall P, et al. Volumetric mammographic density: heritability and association with breast cancer susceptibility loci. J Natl Cancer Inst. 2014;106(12):dju334.

    Article  PubMed  Google Scholar 

  33. Haakensen VD, Biong M, Lingjærde OC, Holmen MM, Frantzen JO, Chen Y, et al. Expression levels of uridine 5’-diphospho-glucuronosyltransferase genes in breast tissue from healthy women are associated with mammographic density. Breast Cancer Res. 2010;12:R65.

    Article  PubMed  PubMed Central  Google Scholar 

  34. Cheasley D, Devereux L, Hughes S, Nickson C, Procopio P, Lee G, et al. The TP53 mutation rate differs in breast cancers that arise in women with high or low mammographic density. NPJ Breast Cancer. 2020;6:34.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  35. Crandall CJ, Sehl ME, Crawford SL, Gold EB, Habel LA, Butler LM, et al. Sex steroid metabolism polymorphisms and mammographic density in pre- and early perimenopausal women. Breast Cancer Res. 2009;11:R51.

    Article  PubMed  PubMed Central  Google Scholar 

  36. Li S, Nguyen TL, Nguyen-Dumont T, Dowty JG, Dite GS, Ye Z, et al. Genetic aspects of mammographic density measures associated with breast cancer risk. Cancers. 2022;14(11):2767.

    Article  PubMed  PubMed Central  Google Scholar 

  37. Khorshid Shamshiri A, Alidoust M, Hemmati Nokandei M, Pasdar A, Afzaljavan F. Genetic architecture of mammographic density as a risk factor for breast cancer: a systematic review. Clin Translational Oncol. 2023;25(6):1729–47.

    Article  CAS  Google Scholar 

  38. Yaghjyan L, Colditz GA, Rosner B, Bertrand KA, Tamimi RM. Reproductive factors related to childbearing and mammographic breast density. Breast Cancer Res Treat. 2016;158(2):351–9.

    Article  PubMed  PubMed Central  Google Scholar 

  39. Pastore E, Caini S, Bendinelli B, Palli D, Ermini I, de Bonfioli Cavalcabo N, et al. Dietary patterns, dietary interventions, and mammographic breast density: a systematic literature review. Nutrients. 2022;14(24):5312.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  40. Yaghjyan L, Colditz G, Rosner B, Rich S, Egan K, Tamimi RM. Adolescent caffeine consumption and mammographic breast density in premenopausal women. Eur J Nutr. 2020;59(4):1633–9.

    Article  PubMed  CAS  Google Scholar 

  41. Martin LJ, Minkin S, Boyd NF. Hormone therapy, mammographic density, and breast cancer risk. Maturitas. 2009;64(1):20–6.

    Article  PubMed  CAS  Google Scholar 

  42. Pike MC, Krailo MD, Henderson BE, Casagrande JT, Hoel DG. Hormonal’ risk factors, ‘breast tissue age’ and the age-incidence of breast cancer. Nature. 1983;303(5920):767–70.

    Article  PubMed  CAS  Google Scholar 

  43. Gusterson BA, Stein T. Human breast development. Semin Cell Dev Biol. 2012;23(5):567–73.

    Article  PubMed  Google Scholar 

  44. Ghadge AG, Dasari P, Stone J, Thompson EW, Robker RL, Ingman WV. Pubertal mammary gland development is a key determinant of adult mammographic density. Semin Cell Dev Biol. 2021;114:143–58.

    Article  PubMed  Google Scholar 

  45. Houghton LC, Jung S, Troisi R, LeBlanc ES, Snetselaar LG, Hylton NM, et al. Pubertal timing and breast density in young women: a prospective cohort study. Breast Cancer Res. 2019;21:122.

    Article  PubMed  PubMed Central  Google Scholar 

  46. Schoemaker MJ, Jones ME, Allen S, Hoare J, Ashworth A, Dowsett M, et al. Childhood body size and pubertal timing in relation to adult mammographic density phenotype. Breast Cancer Res. 2017;19:13.

    Article  PubMed  PubMed Central  Google Scholar 

  47. Lope V, Pérez-Gómez B, Moreno MP, Vidal C, Salas-Trejo D, Ascunce N, et al. Childhood factors associated with mammographic density in adult women. Breast Cancer Res Treat. 2011;130(3):965–74.

    Article  PubMed  Google Scholar 

  48. McCormack VA, dos Santos Silva I, De Stavola BL, Perry N, Vinnicombe S, Swerdlow AJ, et al. Life-course body size and perimenopausal mammographic parenchymal patterns in the MRC 1946 British birth cohort. Br J Cancer. 2003;89(5):852–9.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  49. Dite GS, Gurrin LC, Byrnes GB, Stone J, Gunasekara A, McCredie MR, et al. Predictors of mammographic density: insights gained from a novel regression analysis of a twin study. Cancer Epidemiol Biomarkers Prev. 2008;17(12):3474–81.

    Article  PubMed  PubMed Central  Google Scholar 

  50. Alexeeff SE, Odo NU, Lipson JA, Achacoso N, Rothstein JH, Yaffe MJ, et al. Age at menarche and late adolescent adiposity associated with mammographic density on processed digital mammograms in 24,840 women. Cancer Epidemiol Biomarkers Prev. 2017;26(9):1450–8.

    Article  PubMed  PubMed Central  Google Scholar 

  51. Heng D, Gao F, Jong R, Fishell E, Yaffe M, Martin L, et al. Risk factors for breast cancer associated with mammographic features in Singaporean Chinese women. Cancer Epidemiol Biomarkers Prev. 2004;13(11 Pt 1):1751–8.

    Article  PubMed  Google Scholar 

  52. Kelemen LE, Pankratz VS, Sellers TA, Brandt KR, Wang A, Janney C, et al. Age-specific trends in mammographic density: the Minnesota breast Cancer Family Study. Am J Epidemiol. 2008;167(9):1027–36.

    Article  PubMed  Google Scholar 

  53. Boyd N, Martin L, Stone J, Little L, Minkin S, Yaffe M. A longitudinal study of the effects of menopause on mammographic features. Cancer Epidemiol Biomarkers Prev. 2002;11(10 Pt 1):1048–53.

    PubMed  Google Scholar 

  54. Yan H, Ren W, Jia M, Xue P, Li Z, Zhang S, et al. Breast cancer risk factors and mammographic density among 12518 average-risk women in rural China. BMC Cancer. 2023;23:952.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  55. Dai H, Yan Y, Wang P, Liu P, Cao Y, Xiong L, et al. Distribution of mammographic density and its influential factors among Chinese women. Int J Epidemiol. 2014;43(4):1240–51.

    Article  PubMed  PubMed Central  Google Scholar 

  56. Akinyemiju TF, Tehranifar P, Flom JD, Liao Y, Wei Y, Terry MB. Early life growth, socioeconomic status, and mammographic breast density in an urban US birth cohort. Ann Epidemiol. 2016;26(8):540–e545542.

    Article  PubMed  PubMed Central  Google Scholar 

  57. Yang Y, Liu J, Gu R, Hu Y, Liu F, Yun M, et al. Influence of factors on mammographic density in premenopausal Chinese women. Eur J Cancer Prev. 2016;25(4):306–11.

    Article  PubMed  Google Scholar 

  58. Reeves KW, Stone RA, Modugno F, Ness RB, Vogel VG, Weissfeld JL, et al. Longitudinal association of anthropometry with mammographic breast density in the study of women’s Health across the Nation. Int J Cancer. 2009;124(5):1169–77.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  59. Barnard ME, Martheswaran T, Van Meter M, Buys SS, Curtin K, Doherty JA. Body mass index and mammographic density in a multiracial and multiethnic population-based study. Cancer Epidemiol Biomarkers Prev. 2022;31(7):1313–23.

    Article  PubMed  PubMed Central  Google Scholar 

  60. Jeon JH, Kang JH, Kim Y, Lee HY, Choi KS, Jun JK, et al. Reproductive and hormonal factors associated with fatty or dense breast patterns among Korean women. Cancer Res Treat. 2011;43(1):42–8.

    Article  PubMed  PubMed Central  Google Scholar 

  61. Andersen ZJ, Baker JL, Bihrmann K, Vejborg I, Sørensen TI, Lynge E. Birth weight, childhood body mass index, and height in relation to mammographic density and breast cancer: a register-based cohort study. Breast Cancer Res. 2014;16:R4.

    Article  PubMed  PubMed Central  Google Scholar 

  62. Frydenberg H, Flote VG, Larsson IM, Barrett ES, Furberg AS, Ursin G, et al. Alcohol consumption, endogenous estrogen and mammographic density among premenopausal women. Breast Cancer Res. 2015;17:103.

    Article  PubMed  PubMed Central  Google Scholar 

  63. Maskarinec G, Takata Y, Pagano I, Lurie G, Wilkens LR, Kolonel LN. Alcohol consumption and mammographic density in a multiethnic population. Int J Cancer. 2006;118(10):2579–83.

    Article  PubMed  CAS  Google Scholar 

  64. Okamoto T, Ito A. Association between alcohol consumption and mammographic density: a hospital-based cross-sectional study. Breast Cancer. 2019;26(4):478–84.

    Article  PubMed  Google Scholar 

  65. Quandt Z, Flom JD, Tehranifar P, Reynolds D, Terry MB, McDonald JA. The association of alcohol consumption with mammographic density in a multiethnic urban population. BMC Cancer. 2015;15:1094.

    Article  PubMed  Google Scholar 

  66. Lee M, Kotake R, Yamauchi H. Physical activity and mammographic density in Japanese women. Cancer Epidemiol Biomarkers Prev. 2024;33(3):365–70.

    Article  PubMed  Google Scholar 

  67. Bremnes Y, Ursin G, Bjurstam N, Gram IT. Different measures of smoking exposure and mammographic density in postmenopausal Norwegian women: a cross-sectional study. Breast Cancer Res. 2007;9:R73.

    Article  PubMed  PubMed Central  Google Scholar 

  68. Polyak K, Kalluri R. The role of the microenvironment in mammary gland development and cancer. Cold Spring Harb Perspect Biol. 2010;2(11):a003244.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  69. Archer M, Dasari P, Evdokiou A, Ingman WV. Biological mechanisms and therapeutic opportunities in mammographic density and breast cancer risk. Cancers. 2021;13(21):5391.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  70. Boyd NF, Jensen HM, Cooke G, Han HL. Relationship between mammographic and histological risk factors for breast cancer. J Natl Cancer Inst. 1992;84(15):1170–9.

    Article  PubMed  CAS  Google Scholar 

  71. Bright RA, Morrison AS, Brisson J, Burstein NA, Sadowsky NS, Kopans DB, et al. Relationship between mammographic and histologic features of breast tissue in women with benign biopsies. Cancer. 1988;61(2):266–71.

    Article  PubMed  CAS  Google Scholar 

  72. Li T, Sun L, Miller N, Nicklee T, Woo J, Hulse-Smith L, et al. The association of measured breast tissue characteristics with mammographic density and other risk factors for breast cancer. Cancer Epidemiol Biomarkers Prev. 2005;14(2):343–9.

    Article  PubMed  Google Scholar 

  73. Boyd NF, Lockwood GA, Byng JW, Tritchler DL, Yaffe MJ. Mammographic densities and breast cancer risk. Cancer Epidemiol Biomarkers Prev. 1998;7(12):1133–44.

    PubMed  CAS  Google Scholar 

  74. Huo CW, Chew G, Hill P, Huang D, Ingman W, Hodson L, et al. High mammographic density is associated with an increase in stromal collagen and immune cells within the mammary epithelium. Breast Cancer Res. 2015;17:79.

    Article  PubMed  PubMed Central  Google Scholar 

  75. Fernández-Nogueira P, Mancino M, Fuster G, Bragado P, Puig MP, Gascón P, et al. Breast mammographic density: stromal implications on breast cancer detection and therapy. J Clin Med. 2020;9(3):776.

    Article  PubMed  PubMed Central  Google Scholar 

  76. Provenzano PP, Inman DR, Eliceiri KW, Knittel JG, Yan L, Rueden CT, et al. Collagen density promotes mammary tumor initiation and progression. BMC Med. 2008;6:11.

    Article  PubMed  PubMed Central  Google Scholar 

  77. Hawes D, Downey S, Pearce CL, Bartow S, Wan P, Pike MC, et al. Dense breast stromal tissue shows greatly increased concentration of breast epithelium but no increase in its proliferative activity. Breast Cancer Res. 2006;8:R24.

    Article  PubMed  PubMed Central  Google Scholar 

  78. Ghosh K, Brandt KR, Reynolds C, Scott CG, Pankratz VS, Riehle DL, et al. Tissue composition of mammographically dense and non-dense breast tissue. Breast Cancer Res Treat. 2012;131(1):267–75.

    Article  PubMed  Google Scholar 

  79. Alowami S, Troup S, Al-Haddad S, Kirkpatrick I, Watson PH. Mammographic density is related to stroma and stromal proteoglycan expression. Breast Cancer Res. 2003;5:R129–135.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  80. Bavik C, Coleman I, Dean JP, Knudsen B, Plymate S, Nelson PS. The gene expression program of prostate fibroblast senescence modulates neoplastic epithelial cell proliferation through paracrine mechanisms. Cancer Res. 2006;66(2):794–802.

    Article  PubMed  CAS  Google Scholar 

  81. Archer M, Dasari P, Walsh D, Britt KL, Evdokiou A, Ingman WV. Immune regulation of mammary fibroblasts and the impact of mammographic density. J Clin Med. 2022;11(3):799.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  82. Yang WT, Lewis MT, Hess K, Wong H, Tsimelzon A, Karadag N, et al. Decreased TGFbeta signaling and increased COX2 expression in high risk women with increased mammographic breast density. Breast Cancer Res Treat. 2010;119(2):305–14.

    Article  PubMed  CAS  Google Scholar 

  83. Bodelon C, Mullooly M, Pfeiffer RM, Fan S, Abubakar M, Lenz P, et al. Mammary collagen architecture and its association with mammographic density and lesion severity among women undergoing image-guided breast biopsy. Breast Cancer Res. 2021;23:105.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  84. Ng MR, Brugge JS. A stiff blow from the stroma: collagen crosslinking drives tumor progression. Cancer Cell. 2009;16(6):455–7.

    Article  PubMed  CAS  Google Scholar 

  85. Gao H, Tian Q, Zhu L, Feng J, Zhou Y, Yang J. 3D extracellular matrix regulates the activity of T cells and cancer associated fibroblasts in breast cancer. Front Oncol. 2021;11:764204.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  86. Levental KR, Yu H, Kass L, Lakins JN, Egeblad M, Erler JT, et al. Matrix crosslinking forces tumor progression by enhancing integrin signaling. Cell. 2009;139(5):891–906.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  87. Vallet SD, Ricard-Blum S. Lysyl oxidases: from enzyme activity to extracellular matrix cross-links. Essays Biochem. 2019;63(3):349–64.

    Article  PubMed  CAS  Google Scholar 

  88. Tarchi SM, Pernia Marin M, Hossain MM, Salvatore M. Breast stiffness, a risk factor for cancer and the role of radiology for diagnosis. J Translational Med. 2023;21:582.

    Article  Google Scholar 

  89. Provenzano PP, Inman DR, Eliceiri KW, Keely PJ. Matrix density-induced mechanoregulation of breast cell phenotype, signaling and gene expression through a FAK-ERK linkage. Oncogene. 2009;28(49):4326–43.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  90. Das A, Yaqoob U, Mehta D, Shah VH. FXR promotes endothelial cell motility through coordinated regulation of FAK and MMP-9. Arterioscler Thromb Vasc Biol. 2009;29(4):562–70.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  91. Franchi M, Piperigkou Z, Karamanos KA, Franchi L, Masola V. Extracellular matrix-mediated breast cancer cells morphological alterations, invasiveness, and microvesicles/exosomes release. Cells. 2020;9(9):2031.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  92. Appunni S, Rubens M, Ramamoorthy V, Anand V, Khandelwal M, Saxena A, et al. Lumican, pro-tumorigenic or anti-tumorigenic: a conundrum. Clin Chim Acta. 2021;514:1–7.

    Article  PubMed  CAS  Google Scholar 

  93. Hu X, Villodre ES, Larson R, Rahal OM, Wang X, Gong Y, et al. Decorin-mediated suppression of tumorigenesis, invasion, and metastasis in inflammatory breast cancer. Commun Biology. 2021;4:72.

    Article  CAS  Google Scholar 

  94. Nazari SS, Mukherjee P. An overview of mammographic density and its association with breast cancer. Breast Cancer. 2018;25(3):259–67.

    Article  PubMed  Google Scholar 

  95. Skandalis SS, Labropoulou VT, Ravazoula P, Likaki-Karatza E, Dobra K, Kalofonos HP, et al. Versican but not decorin accumulation is related to malignancy in mammographically detected high density and malignant-appearing microcalcifications in non-palpable breast carcinomas. BMC Cancer. 2011;11:314.

    Article  PubMed  PubMed Central  Google Scholar 

  96. Lundberg P, Forsgren MF, Tellman J, Kihlberg J, Rzepecka A, Dabrosin C. Breast density is strongly associated with multiparametric magnetic resonance imaging biomarkers and pro-tumorigenic proteins in situ. Br J Cancer. 2022;127(11):2025–33.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  97. Abrahamsson A, Rzepecka A, Romu T, Borga M, Leinhard OD, Lundberg P, et al. Dense breast tissue in postmenopausal women is associated with a pro-inflammatory microenvironment in vivo. Oncoimmunology. 2016;5(10):e1229723.

    Article  PubMed  PubMed Central  Google Scholar 

  98. Maskarinec G, Ju D, Fong J, Horio D, Chan O, Loo LWM, et al. Mammographic density and breast tissue expression of inflammatory markers, growth factors, and vimentin. BMC Cancer. 2018;18:1191.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  99. Coussens LM, Pollard JW. Leukocytes in mammary development and cancer. Cold Spring Harb Perspect Biol. 2011;3(3):a003285.

    Article  PubMed  PubMed Central  Google Scholar 

  100. Sun X, Glynn DJ, Hodson LJ, Huo C, Britt K, Thompson EW, et al. CCL2-driven inflammation increases mammary gland stromal density and cancer susceptibility in a transgenic mouse model. Breast Cancer Res. 2017;19:4.

    Article  PubMed  PubMed Central  Google Scholar 

  101. DeNichilo MO, Panagopoulos V, Rayner TE, Borowicz RA, Greenwood JE, Evdokiou A. Peroxidase enzymes regulate collagen extracellular matrix biosynthesis. Am J Pathol. 2015;185(5):1372–84.

    Article  PubMed  CAS  Google Scholar 

  102. García-Mendoza MG, Inman DR, Ponik SM, Jeffery JJ, Sheerar DS, Van Doorn RR, et al. Neutrophils drive accelerated tumor progression in the collagen-dense mammary tumor microenvironment. Breast Cancer Res. 2016;18:49.

    Article  PubMed  PubMed Central  Google Scholar 

  103. Huo CW, Hill P, Chew G, Neeson PJ, Halse H, Williams ED, et al. High mammographic density in women is associated with protumor inflammation. Breast Cancer Res. 2018;20:92.

    Article  PubMed  PubMed Central  Google Scholar 

  104. Fortner RT, Sisti J, Chai B, Collins LC, Rosner B, Hankinson SE, et al. Parity, breastfeeding, and breast cancer risk by hormone receptor status and molecular phenotype: results from the nurses’ Health studies. Breast Cancer Res. 2019;21:40.

    Article  PubMed  PubMed Central  Google Scholar 

  105. Sangaramoorthy M, Hines LM, Torres-Mejía G, Phipps AI, Baumgartner KB, Wu AH, et al. A pooled analysis of breastfeeding and breast cancer risk by hormone receptor status in parous hispanic women. Epidemiology. 2019;30(3):449–57.

    Article  PubMed  PubMed Central  Google Scholar 

  106. Lin H, Wen J, Hong L, Chen Y, Wu Y, Zhong S. Synergistic effect between full-term pregnancy/breastfeeding and familial susceptibility on breast cancer risk. Cancer Manage Res. 2019;11:9743–8.

    Article  CAS  Google Scholar 

  107. Stordal B. Breastfeeding reduces the risk of breast cancer: a call for action in high-income countries with low rates of breastfeeding. Cancer Med. 2023;12(4):4616–25.

    Article  PubMed  Google Scholar 

  108. Figueroa JD, Davis Lynn BC, Edusei L, Titiloye N, Adjei E, Clegg-Lamptey JN, et al. Reproductive factors and risk of breast cancer by tumor subtypes among Ghanaian women: a population-based case-control study. Int J Cancer. 2020;147(6):1535–47.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  109. Dydjow-Bendek DA, Zagożdżon P. Early alcohol use initiation, obesity, not breastfeeding, and residence in a rural area as risk factors for breast cancer: a case-control study. Cancers. 2021;13(16):3925.

    Article  PubMed  PubMed Central  Google Scholar 

  110. Islami F, Liu Y, Jemal A, Zhou J, Weiderpass E, Colditz G, et al. Breastfeeding and breast cancer risk by receptor status–a systematic review and meta-analysis. Ann Oncol. 2015;26(12):2398–407.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  111. Friebel TM, Domchek SM, Rebbeck TR. Modifiers of cancer risk in BRCA1 and BRCA2 mutation carriers: systematic review and meta-analysis. J Natl Cancer Inst. 2014;106(6):dju091.

    Article  PubMed  PubMed Central  Google Scholar 

  112. Jernström H, Lubinski J, Lynch HT, Ghadirian P, Neuhausen S, Isaacs C, et al. Breast-feeding and the risk of breast cancer in BRCA1 and BRCA2 mutation carriers. J Natl Cancer Inst. 2004;96(14):1094–8.

    Article  PubMed  Google Scholar 

  113. Palmer JR, Viscidi E, Troester MA, Hong CC, Schedin P, Bethea TN, et al. Parity, lactation, and breast cancer subtypes in African American women: results from the AMBER Consortium. J Natl Cancer Inst. 2014;106(10):dju237.

    Article  PubMed  PubMed Central  Google Scholar 

  114. Lefrère H, Lenaerts L, Borges VF, Schedin P, Neven P, Amant F. Postpartum breast cancer: mechanisms underlying its worse prognosis, treatment implications, and fertility preservation. Int J Gynecol Cancer. 2021;31(3):412–22.

    Article  PubMed  PubMed Central  Google Scholar 

  115. Borges VF, Lyons TR, Germain D, Schedin P. Postpartum involution and cancer: an opportunity for targeted breast cancer prevention and treatments? Cancer Res. 2020;80(9):1790–8.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  116. Schedin P. Pregnancy-associated breast cancer and metastasis. Nat Rev Cancer. 2006;6(4):281–91.

    Article  PubMed  CAS  Google Scholar 

  117. Li CI, Beaber EF, Tang MT, Porter PL, Daling JR, Malone KE. Reproductive factors and risk of estrogen receptor positive, triple-negative, and HER2-neu overexpressing breast cancer among women 20–44 years of age. Breast Cancer Res Treat. 2013;137(2):579–87.

    Article  PubMed  CAS  Google Scholar 

  118. Anstey EH, Shoemaker ML, Barrera CM, O’Neil ME, Verma AB, Holman DM. Breastfeeding and breast cancer risk reduction: implications for black mothers. Am J Prev Med. 2017;53(3s1):S40–6.

    Article  PubMed  PubMed Central  Google Scholar 

  119. Alexeeff SE, Odo NU, McBride R, McGuire V, Achacoso N, Rothstein JH, et al. Reproductive factors and mammographic density: associations among 24,840 women and comparison of studies using digitized film-screen mammography and full-field digital mammography. Am J Epidemiol. 2019;188(6):1144–54.

    Article  PubMed  PubMed Central  Google Scholar 

  120. Chen Y, Jiang P, Geng Y. The role of breastfeeding in breast cancer prevention: a literature review. Front Oncol. 2023;13:1257804.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  121. Sherman ME, Levi M, Teras LR. Reproductive events and risk of women’s cancers: from parturition to prevention. Cancer Prev Res. 2023;16(6):309–12.

    Article  Google Scholar 

  122. Getz KR, Adedokun B, Xu S, Toriola AT. Breastfeeding and mammographic breast density: a cross-sectional study. Cancer Prev Res. 2023;16(6):353–61.

    Article  Google Scholar 

  123. Shang MY, Guo S, Cui MK, Zheng YF, Liao ZX, Zhang Q, et al. Influential factors and prediction model of mammographic density among Chinese women. Medicine. 2021;100(28):e26586.

    Article  PubMed  PubMed Central  Google Scholar 

  124. Nakajima E, Iwase T, Miyagi Y, Fujita T, Ikeda N, Ishikawa T, et al. Association of parity and infant feeding method with breast density on mammography. Acad Radiol. 2020;27(2):e24–6.

    Article  PubMed  Google Scholar 

  125. Sung H, Ren J, Li J, Pfeiffer RM, Wang Y, Guida JL, et al. Breast cancer risk factors and mammographic density among high-risk women in urban China. NPJ Breast Cancer. 2018;4:3.

    Article  PubMed  PubMed Central  Google Scholar 

  126. Ursin G, Sun CL, Koh WP, Khoo KS, Gao F, Wu AH, et al. Associations between soy, diet, reproductive factors, and mammographic density in Singapore Chinese women. Nutr Cancer. 2006;56(2):128–35.

    Article  PubMed  CAS  Google Scholar 

  127. Modugno F, Ngo DL, Allen GO, Kuller LH, Ness RB, Vogel VG, et al. Breast cancer risk factors and mammographic breast density in women over age 70. Breast Cancer Res Treat. 2006;97(2):157–66.

    Article  PubMed  Google Scholar 

  128. Riza E, dos Santos Silva I, De Stavola B, Perry N, Karadedou-Zafiriadou E, Linos D, et al. Correlates of high-density mammographic parenchymal patterns by menopausal status in a rural population in Northern Greece. Eur J Cancer. 2005;41(4):590–600.

    Article  PubMed  Google Scholar 

  129. Basree MM, Shinde N, Koivisto C, Cuitino M, Kladney R, Zhang J, et al. Abrupt involution induces inflammation, estrogenic signaling, and hyperplasia linking lack of breastfeeding with increased risk of breast cancer. Breast Cancer Res. 2019;21:80.

    Article  PubMed  PubMed Central  Google Scholar 

  130. Stecklein SR, Reddy JP, Wolfe AR, Lopez MS, Fouad TM, Debeb BG, et al. Lack of breastfeeding history in parous women with inflammatory breast cancer predicts poor disease-free survival. J Cancer. 2017;8(10):1726–32.

    Article  PubMed  PubMed Central  Google Scholar 

  131. Bouras T, Pal B, Vaillant F, Harburg G, Asselin-Labat ML, Oakes SR, et al. Notch signaling regulates mammary stem cell function and luminal cell-fate commitment. Cell Stem Cell. 2008;3(4):429–41.

    Article  PubMed  CAS  Google Scholar 

  132. Takabatake Y, Oxvig C, Nagi C, Adelson K, Jaffer S, Schmidt H, et al. Lactation opposes pappalysin-1-driven pregnancy-associated breast cancer. EMBO Mol Med. 2016;8(4):388–406.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  133. Mattei AL, Bailly N, Meissner A. DNA methylation: a historical perspective. Trends Genet. 2022;38(7):676–707.

    Article  PubMed  CAS  Google Scholar 

  134. Wong KK. DNMT1: a key drug target in triple-negative breast cancer. Sem Cancer Biol. 2021;72:198–213.

    Article  CAS  Google Scholar 

  135. Caini S, Fiorito G, Palli D, Bendinelli B, Polidoro S, Silvestri V, et al. Pre-diagnostic DNA methylation patterns differ according to mammographic breast density amongst women who subsequently develop breast cancer: a case-only study in the EPIC-Florence cohort. Breast Cancer Res Treat. 2021;189(2):435–44.

    Article  PubMed  CAS  Google Scholar 

  136. Chen Z, Luo J, Zhang C, Ma Y, Sun S, Zhang T, et al. Mechanism of prolactin inhibition of miR-135b via methylation in goat mammary epithelial cells. J Cell Physiol. 2018;233(1):651–62.

    Article  PubMed  CAS  Google Scholar 

  137. Wang J, Bian Y, Wang Z, Li D, Wang C, Li Q, et al. MicroRNA-152 regulates DNA methyltransferase 1 and is involved in the development and lactation of mammary glands in dairy cows. PLoS ONE. 2014;9(7):e101358.

    Article  PubMed  PubMed Central  Google Scholar 

  138. Bian Y, Lei Y, Wang C, Wang J, Wang L, Liu L, et al. Epigenetic regulation of miR-29s affects the lactation activity of dairy cow mammary epithelial cells. J Cell Physiol. 2015;230(9):2152–63.

    Article  PubMed  CAS  Google Scholar 

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Acknowledgements

The Fig. 1 was drawn by using Figdraw.

Funding

Our study was funded by Liaoning Cancer Hospital - Dalian University of Technology “Medical-Engineering Interdisciplinary Research Fund” Project (Grant ID: LD202226).

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Author notes

  1. Dong-Man Ye and Xiaoru Bai contributed equally to this work.

  2. Tao Yu and Huijian Wu jointly supervised this work.

Authors and Affiliations

  1. Department of Medical Imaging, Cancer Hospital of Dalian University of Technology, Liaoning Cancer Hospital & Institute, No.44 Xiaoheyan Road, Dadong District, Shenyang, 110042, China

    Dong-Man Ye, Xiaoru Bai, Shu Xu, Ning Qu, Nannan Zhao & Tao Yu

  2. Department of Laboratory Medicine, Cancer Hospital of Dalian University of Technology, Liaoning Cancer Hospital & Institute, No.44 Xiaoheyan Road, Dadong District, Shenyang, 110042, China

    Yang Zheng

  3. School of Bioengineering & Key Laboratory of Protein Modification and Disease, Dalian University of Technology, Dalian, 116024, Liaoning Province, China

    Huijian Wu

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  1. Dong-Man Ye
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Contributions

Writing – Original Draft Preparation: Dong-Man Ye and Xiaoru BaiInvestigation: Shu Xu, Ning Qu and Nannan ZhaoConceptualization: Yang ZhengWriting – Review & Editing: Tao Yu and Huijian Wu.

Corresponding authors

Correspondence to Tao Yu or Huijian Wu.

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This review involves the use of patient images for Fig. 2, which was approved by the Medical Ethical Committee of Liaoning Cancer Hospital and Institute (20220327G).

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Ye, DM., Bai, X., Xu, S. et al. Association between breastfeeding, mammographic density, and breast cancer risk: a review. Int Breastfeed J 19, 65 (2024). https://doi.org/10.1186/s13006-024-00672-7

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Keywords

International Breastfeeding Journal

ISSN: 1746-4358