Dense breast tissue in screened postmenopausal women ==================================================== * Ibrahem H. Kanbayti ## Prevalence and determinants ## Abstract **Objectives:** To explore the prevalence of dense breast tissue among screened postmenopausal women and identify the factors influencing breast density in this population. **Methods:** A retrospective analysis of data from postmenopausal women screened for breast cancer in Jeddah, Saudi Arabia, between April 2017 and June 2021 was carried out. Breast density was subjectively assessed, and influencing factors were retrieved from the hospital information system. Proportions were used for descriptive analysis, and binary logistic regression was used to identify the determinants of dense breast tissue. **Results:** Only 12.7% of the postmenopausal women had dense breast tissue. Non-Saudi women (odds ratio [OR]=1.95, 95% confidence interval [CI]: [1.07-3.54], *p*=0.02) and those who did not breastfeed (OR=2.75, 95% CI: [1.33-5.53], *p*=0.006) had a greater likelihood of having dense breast tissue. Women who had never been pregnant (nulliparous) were 4 times more likely to have dense breast tissue than those who had been pregnant (parous; *p*<0.001). Additionally, women with fewer children had a higher chance of dense breast tissue (OR=2.58, 95% CI: [1.23-5.40], *p*=0.01). **Conclusion:** The prevalence of dense breast tissue among screened postmenopausal women was low. However, certain factors increase the risk of having dense tissue in this population, including not being Saudi Arabian, never having breastfed, being nulliparous, and having fewer children. Keywords: * postmenopausal women * mammographic density * mammography * reproductive factors **G**lobally, breast cancer (BC) is the most prevalent cancer in women, with 2.3 million new cases and more than half a million deaths reported in 2020.1 It accounts for less than one-third of cancers diagnosed in Saudi Arabia.2 Early identification facilitated by mammography expands the array of available treatment options, enhances treatment success rates, and improves overall survival rates.3 However, the accuracy of mammography in detecting lesions can be influenced by breast density. Among the various factors influencing BC risk, breast density, which is the relative amount of fibrous and glandular tissue compared with fatty tissue within a woman’s breasts, has emerged as a crucial indicator, with higher density being linked to a higher risk of BC.4 Breast density is a dynamic trait that changes over time, particularly with age.5 Typically, older women have lower breast density than younger women, with the most significant changes occurring during menopausal transition. Despite this trend, a subset of postmenopausal women may experience persistently high breast densities. Studies have shown that BC found in postmenopausal women with dense breasts tends to be more aggressive.6,7 Since dense breast tissue can hinder mammography accuracy and increase the risk of BC, it is vital to understand its prevalence among postmenopausal-screened women. Additionally, identifying the factors that contribute to denser breasts in this population and using this knowledge to assess patients with these risk factors are essential for early BC detection. The influence of reproductive, demographic, and anthropometric factors on mammographic density has been a major area of investigation, with specific emphasis on premenopausal women. Existing research has consistently revealed associations between certain factors and breast density. Specifically, skipping breastfeeding, having a lower parity and pregnancy count, and increased age have been linked to higher breast density, whereas body mass index (BMI) and age tend to exhibit an inverse relationship with breast density.8-10 Despite extensive evidence being available in this area, the correlation between these factors and mammographic density among postmenopausal women remains relatively unexplored. To date, only one study carried out among the Turkish population, has specifically investigated the associations of anthropometric and reproductive factors with breast density in postmenopausal women.11 It revealed that lower BMI and a lower number of pregnancies serve as potential predictors of dense breast tissues in postmenopausal women. These results highlight the need for further research to better understand the complex interplay between reproductive history, demographic characteristics, anthropometric measures, and breast density in postmenopausal women. This study aimed to explore the prevalence of dense breast tissue among postmenopausal-screened women and the factors influencing its density. Investigating breast density in postmenopausal women may reveal important implications for identifying women at a high risk of developing BC. ## Methods At the Sheikh Mohammed Hussien ALAmoudi Center of Excellence in Breast Cancer, King Abdulaziz University, Jeddah, Saudi Arabia, 908 women were screened for BC between April 2017 and June 2021. Among them, 410 fulfilled the eligibility criteria for inclusion, including postmenopausal status and bilateral mammographic density assessment. The exclusion criteria were premenopausal women (n=80), individuals without recorded mammographic density readings (n=175) as indicated in radiology reports, and women with a history of BC, breast surgery, benign breast conditions, or radiotherapy (n=243; Figure 1). The participants completed a questionnaire that collected details regarding their background and health, including nationality, age, weight, and height (used to calculate BMI); menstruation history (age at first period and menopause); childbirth history (age at first pregnancy, number of children, and breastfeeding); any family history of BC; and administration of hormone replacement therapy (HRT). The mammographic density profile of each woman was retrieved from the radiology report. ![Figure 1](http://smj.org.sa/https://smj.org.sa/content/smj/45/11/1238/F1.medium.gif) [Figure 1](http://smj.org.sa/content/45/11/1238/F1) Figure 1 - Flowchart outlining the eligibility requirements for study participants. This study was approved by the Institutional Review Board of King Abdulaziz University Hospital, Jeddah, Saudi Arabia (reference No: 449-18). Categorization of mammographic density in women was carried out according to the Breast Imaging Reporting and Data System (BI-RADS, 4th edition scheme), which divides breast density into 4 categories: BI-RADS A (almost entirely fatty), BI-RADS B (scattered fibroglandular tissue), BI-RADS C (heterogeneously dense), and BI-RADS D (extremely dense; Figure 2). A dual review process was instituted to ensure the accuracy of breast density assessment. Initially, a radiology resident interpreted the mammography findings, which were then reviewed and verified in detail by a female imaging consultant. This dual review procedure aims to improve the reliability and precision of interpretations, thus ensuring the accuracy of mammographic density assessment. The BI-RADS scale was categorized into low (BI-RADS A and B) and high (BI-RADS C and D) categories to simplify the interpretation of the study findings. ![Figure 2](http://smj.org.sa/https://smj.org.sa/content/smj/45/11/1238/F2.medium.gif) [Figure 2](http://smj.org.sa/content/45/11/1238/F2) Figure 2 - The Breast Imaging Reporting and Data System (BI-RADS 4th edition scheme). ### Statistical analysis Differences in population characteristics, including age at screening, menopause, menarche, first birth, BMI, parity, live children, hormonal replacement therapy, family history of BC, and breastfeeding were analyzed between the dense and non-dense groups using the Chi-square test (X2). Unadjusted logistic regression models were used to explore the association between mammographic density and the population features that exhibited a significant relationship (*p*≤0.05) with mammographic density. Odds ratios (ORs) and 95% confidence intervals (CIs) were used to describe the analyses. All statistical tests were carried out using the Statistical Package for the Social Sciences, version 25.0 (IBM Corp., Armonk, NY, USA). A significance level of *p*<0.05 was used to determine statistical significance. ## Results Table 1 shows the demographic characteristics of the screened women included in the study. On average, their mean age was approximately 55±5.72 years. The mean age at first childbirth was approximately 22±4.94 years, with an average age at menarche of 12.9±1.74 years and menopause of 49.07±5.04 years. The participants had an average BMI of approximately 31±12.04 kg/m². Most of the population (68.5%) consisted of Saudi women, with nearly half (46.3%) falling into the 50-60 age group. Notably, half of the women (51.6%) had their first child before the age of 20, and 65.3% experienced menarche between the ages of 12-14 years. Most participants (91.9%) had given birth, with a small percentage (9%) reporting more than 8 children. Breastfeeding was prevalent, as reported by 87.3% of participants, whereas a minority (21%) had undergone HRT. A family history of BC was reported in 19.5% of screened women. The distribution of BMI revealed a higher prevalence of overweight (33.2%) and obesity (53.5%) among the participants. A minority of the women (12.7%) had dense breasts (Figure 3). View this table: [Table 1](http://smj.org.sa/content/45/11/1238/T1) Table 1 - Population characteristics. ![Figure 3](http://smj.org.sa/https://smj.org.sa/content/smj/45/11/1238/F3.medium.gif) [Figure 3](http://smj.org.sa/content/45/11/1238/F3) Figure 3 - Breast density distribution (dense and non-dense breast tissue groups). Table 2 displays variations in breast density across different population characteristics. Among the screened women, those with dense breasts were more likely to be non-Saudi (*p*=0.02), nulliparous (*p*<0.001), have fewer children (*p*=0.03), skip breastfeeding (*p*=0.004), and have a lower BMI (*p*=0.03). However, factors such as age, HRT administration, and family history of BC did not seem to influence breast density (*p*≥0.08). View this table: [Table 2](http://smj.org.sa/content/45/11/1238/T2) Table 2 - Differences in population characteristics by mammographic density status. Table 3 illustrates the determinants of dense breast tissue in postmenopausal women. Compared with Saudi women and those who breastfed, non-Saudi women (OR=1.95; 95% CI: [1.07-3.54]; *p*=0.02) and those who did not breastfeed (OR=2.75; 95% CI: [1.33-5.53]; *p*=0.006) had an increased likelihood of dense breast tissue. Compared with parous women, nulliparous individuals exhibited a 4-fold higher probability of having dense breast tissue (*p*<0.001). Additionally, those with fewer children (OR=2.58; 95% CI: [1.23-5.40]; *p*=0.01) showed higher odds of dense breast tissue. Notably, no significant variation was observed between breast density and BMI classifications (*p*≥0.07). View this table: [Table 3](http://smj.org.sa/content/45/11/1238/T3) Table 3 - Predictors of dense breast tissue among postmenopausal women. ## Discussion The findings of the current study indicate that screened postmenopausal women in Saudi Arabia typically demonstrate low breast density. However, a minority of women exhibit dense breast tissue, notably, those who are non-Saudi, nulliparous, have lower parity, and abstain from breastfeeding. The unexpected finding of dense breasts in some postmenopausal women highlights the complexity of breast density and the need for further research. Although age and menopause typically lead to a decrease in density, other factors may influence this trend. For example, HRT influences breast density. Although not statistically significant in our study, there was a slight increase in the number of women using HRT who exhibited denser breasts. The use of HRT among postmenopausal women potentially impedes the natural process of breast involution that typically occurs with aging, consequently leading to a sustained high breast density. Although this finding implies a potential association between HRT and increased breast density, further research is required to validate this observation. Genetic predisposition and inflammation may increase breast density in postmenopausal women. Variations or mutations in genes such as *BRCA1* and *BRCA2*, which are involved in estrogen metabolism, potentially contribute to sustained breast density.12 These genetic alterations may disrupt estrogen breakdown processes, leading to prolonged exposure of breast tissue to active estrogen postmenopausally and facilitating the development of denser tissue. Furthermore, elevated levels of inflammatory markers, such as interleukin-6 (IL-6), in the breasts of postmenopausal women have been linked to greater breast density.13,14 This link may be explained by the connection between inflammation and local estrogen production. The IL-6 may stimulate the production of an enzyme called aromatase, which converts other hormones into estrogen within the breast tissue. This potential pathway suggests a possible explanation for how inflammation contributes to higher breast density in postmenopausal women, although the overall estrogen levels typically decline post-menopause. Unfortunately, the current study had limited data regarding genetic factors and inflammatory markers. Therefore, further research is required to gain a deeper understanding of the complex interplay between genetic factors and breast density in postmenopausal women. To date, only a single study focusing on Turkish postmenopausal women has explored dense breast tissue among postmenopausal women.11 The results of that study support the current study’s observation that postmenopausal women with dense breast tissue tend to have had fewer pregnancies and live births. Despite this correlation, the mechanisms underlying higher breast density observed in postmenopausal women with these reproductive characteristics remain largely unknown. One potential explanation for this phenomenon is related to hormonal changes associated with reproductive history. For instance, nulliparity, skipping breastfeeding, and delayed childbearing may lead to prolonged exposure of postmenopausal women to endogenous estrogen and progesterone, because these hormones are known to influence breast tissue composition and density.15 Therefore, it is plausible that extended exposure to estrogen and progesterone due to reproductive factors could contribute to the higher mammographic density observed in postmenopausal women with these reproductive characteristics. Further studies are required to elucidate the precise mechanisms underlying this relationship. Our findings validate previous research indicating that Saudi women, as a group, tend to exhibit low levels of breast density.16,17 The current study also reveals significant national differences in mammographic density among postmenopausal women undergoing screening. Variations in mammographic density between Saudi women and individuals of other nationalities were observed, which is consistent with the findings of Albeshan et al.18 Their research indicated that Arab women, including Saudis, generally exhibit a lower mammographic density than women of Asian and African nations. These national variations in mammographic density underscore the necessity of considering national differences in BC screening and risk assessments in the Saudi population. Understanding these differences can inform personalized screening strategies and risk assessment models tailored to specific racial and ethnic groups. Further investigation into the underlying factors contributing to these disparities may provide valuable insights into BC risk and prevention strategies across diverse populations. Our study findings align with prior research indicating that postmenopausal women with lower BMI often exhibit denser breast tissue.11 Although the precise mechanism remains unclear, one potential explanation lies in the association between BMI and fatty tissue within the breasts. Studies have consistently shown a positive relationship between BMI and the presence of fatty breast tissue.19-21 Consequently, higher BMI levels are typically linked to increased non-dense breast tissue. Thus, the observed reduction in breast density among our study participants, particularly among those classified as obese, may be attributed to the direct relationship between BMI and non-dense breast tissue. Our findings hold significant promise for improving the detection and prevention of BC in postmenopausal Saudi Arabian women. First, identifying factors associated with higher breast density can help tailor screening strategies. Currently, mammography is less effective in women with dense breast tissue. Knowing who might fall into this category may allow us to consider additional methods such as ultrasonography or MRI in combination with mammography for these women, potentially leading to earlier and more accurate cancer detection. Secondly, these findings lay the groundwork for future research on lifestyle modifications as a potential preventive strategy. While more research, particularly large-scale longitudinal studies, is needed to confirm these links, our findings suggest a possible correlation of factors such as childbirth, breastfeeding, and birth control use with breast density. Understanding these relationships could inform future recommendations for women when considering these choices. This knowledge can empower healthcare professionals to tailor strategies based on individual risk factors, ultimately contributing to improved patient outcomes. Our investigation offers distinct advantages that may guide personalized BC tactics in Saudi Arabia. Additionally, detailed data on potential influencing factors such as demographics, reproductive history, and body measurements play a role in revealing the predictors of dense breast tissue among postmenopausal individuals. ### Study limitations While the BI-RADS 4th edition scheme is typically used by radiologists for breast density assessment, this method can be subjective and prone to variations between observers. However, the dual review process used in this study may address the potential benefits of breast density assessment. Another limitation is the lack of genetic and breast tissue inflammation marker data, which have been linked to a higher density in postmenopausal women.13,14,22 In conclusion, our findings revealed a very low prevalence of dense breast tissue among screened postmenopausal women. Furthermore, the study revealed that being non-Saudi, being nulliparous, having fewer children, and skipping breastfeeding were correlated with increased breast density in this population. Although these results hold promise for the development of personalized BC screening and prevention strategies in postmenopausal women in Saudi Arabia, further research is required to confirm these findings in larger and more diverse populations. ## Acknowledgment *The authors gratefully acknowledge ContentConcepts for the English language editing.* ## Footnotes * **Disclosure.** Author has no conflict of interests, and the work was not supported or funded by any drug company. * Received July 14, 2024. * Accepted October 13, 2024. * Copyright: © Saudi Medical Journal This is an Open Access journal and articles published are distributed under the terms of the Creative Commons Attribution-NonCommercial License (CC BY-NC). Readers may copy, distribute, and display the work for non-commercial purposes with the proper citation of the original work. ## References 1. 1.Lei S, Zheng R, Zhang S, Wang S, Chen R, Sun K, et al. Global patterns of breast cancer incidence and mortality: a population-based cancer registry data analysis from 2000-2020. Cancer Commun (Lond) 2021; 41: 1183-1194. [PubMed](http://smj.org.sa/lookup/external-ref?access_num=34399040&link_type=MED&atom=%2Fsmj%2F45%2F11%2F1238.atom) 2. 2.Alqahtani WS, Almufareh NA, Domiaty DM, Albasher G, Alduwish MA, Alkhalaf H, et al. Epidemiology of cancer in Saudi Arabia thru 2010-2019: a systematic review with constrained meta-analysis. AIMS Public Health 2020; 7: 679-696. [PubMed](http://smj.org.sa/lookup/external-ref?access_num=32968686&link_type=MED&atom=%2Fsmj%2F45%2F11%2F1238.atom) 3. 3.Kaplan HG, Malmgren JA, Atwood MK, Calip GS. Effect of treatment and mammography detection on breast cancer survival over time: 1990-2007. Cancer 2015; 121: 2553-2561. [CrossRef](http://smj.org.sa/lookup/external-ref?access_num=10.1002/cncr.29371&link_type=DOI) [PubMed](http://smj.org.sa/lookup/external-ref?access_num=25872471&link_type=MED&atom=%2Fsmj%2F45%2F11%2F1238.atom) 4. 4.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: 1159-1169. [Abstract/FREE Full Text](http://smj.org.sa/lookup/ijlink/YTozOntzOjQ6InBhdGgiO3M6MTQ6Ii9sb29rdXAvaWpsaW5rIjtzOjU6InF1ZXJ5IjthOjQ6e3M6ODoibGlua1R5cGUiO3M6NDoiQUJTVCI7czoxMToiam91cm5hbENvZGUiO3M6NDoiY2VicCI7czo1OiJyZXNpZCI7czo5OiIxNS82LzExNTkiO3M6NDoiYXRvbSI7czoyMDoiL3Ntai80NS8xMS8xMjM4LmF0b20iO31zOjg6ImZyYWdtZW50IjtzOjA6IiI7fQ==) 5. 5.Mullooly M, Fan S, Pfeiffer RM, Bowles EA, Duggan MA, Falk RT, et al. Temporal changes in mammographic breast density and breast cancer risk among women with benign breast disease. Breast Cancer Res 2024; 26: 52. [PubMed](http://smj.org.sa/lookup/external-ref?access_num=38532516&link_type=MED&atom=%2Fsmj%2F45%2F11%2F1238.atom) 6. 6.Yaghjyan L, Colditz GA, Collins LC, Schnitt SJ, Rosner B, Vachon C, et al. Mammographic breast density and subsequent risk of breast cancer in postmenopausal women according to tumor characteristics. J Natl Cancer Inst 2011; 103: 1179-1189. [CrossRef](http://smj.org.sa/lookup/external-ref?access_num=10.1093/jnci/djr225&link_type=DOI) [PubMed](http://smj.org.sa/lookup/external-ref?access_num=21795664&link_type=MED&atom=%2Fsmj%2F45%2F11%2F1238.atom) [Web of Science](http://smj.org.sa/lookup/external-ref?access_num=000293628200009&link_type=ISI) 7. 7.Yaghjyan L, Pettersson A, Colditz GA, Collins LC, Schnitt SJ, Beck AH, et al. Postmenopausal mammographic breast density and subsequent breast cancer risk according to selected tissue markers. Br J Cancer 2015; 113: 1104-1113. [PubMed](http://smj.org.sa/lookup/external-ref?access_num=26335607&link_type=MED&atom=%2Fsmj%2F45%2F11%2F1238.atom) 8. 8.Hanis TM, Arifin WN, Haron J, Wan Abdul Rahman WF, Ruhaiyem NIR, Abdullah R, et al. Factors influencing mammographic density in Asian women: a retrospective cohort study in the Northeast region of Peninsular Malaysia. Diagnostics (Basel) 2022; 12: 860. [PubMed](http://smj.org.sa/lookup/external-ref?access_num=35453907&link_type=MED&atom=%2Fsmj%2F45%2F11%2F1238.atom) 9. 9.Getz KR, Adedokun B, Xu S, Toriola AT. Breastfeeding and mammographic breast density: a cross-sectional study. Cancer Prev Res (Phila) 2023; 16: 353-361. [PubMed](http://smj.org.sa/lookup/external-ref?access_num=36930943&link_type=MED&atom=%2Fsmj%2F45%2F11%2F1238.atom) 10. 10.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: 351-359. [PubMed](http://smj.org.sa/lookup/external-ref?access_num=27351801&link_type=MED&atom=%2Fsmj%2F45%2F11%2F1238.atom) 11. 11.Caglayan EK, Caglayan K, Alkis I, Arslan E, Okur A, Banli O, et al. Factors associated with mammographic density in postmenopausal women. J Menopausal Med 2015; 21: 82-88. [PubMed](http://smj.org.sa/lookup/external-ref?access_num=26357645&link_type=MED&atom=%2Fsmj%2F45%2F11%2F1238.atom) 12. 12.Savage KI, Matchett KB, Barros EM, Cooper KM, Irwin GW, Gorski JJ, et al. BRCA1 deficiency exacerbates estrogen-induced DNA damage and genomic instability. Cancer Res 2014; 74: 2773-2784. [Abstract/FREE Full Text](http://smj.org.sa/lookup/ijlink/YTozOntzOjQ6InBhdGgiO3M6MTQ6Ii9sb29rdXAvaWpsaW5rIjtzOjU6InF1ZXJ5IjthOjQ6e3M6ODoibGlua1R5cGUiO3M6NDoiQUJTVCI7czoxMToiam91cm5hbENvZGUiO3M6NjoiY2FucmVzIjtzOjU6InJlc2lkIjtzOjEwOiI3NC8xMC8yNzczIjtzOjQ6ImF0b20iO3M6MjA6Ii9zbWovNDUvMTEvMTIzOC5hdG9tIjt9czo4OiJmcmFnbWVudCI7czowOiIiO30=) 13. 13.Purohit A, Reed MJ. Regulation of estrogen synthesis in postmenopausal women. Steroids 2002; 67: 979-983. [CrossRef](http://smj.org.sa/lookup/external-ref?access_num=10.1016/S0039-128X(02)00046-6&link_type=DOI) [PubMed](http://smj.org.sa/lookup/external-ref?access_num=12398994&link_type=MED&atom=%2Fsmj%2F45%2F11%2F1238.atom) [Web of Science](http://smj.org.sa/lookup/external-ref?access_num=000179236800007&link_type=ISI) 14. 14.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: e1229723. [PubMed](http://smj.org.sa/lookup/external-ref?access_num=27853653&link_type=MED&atom=%2Fsmj%2F45%2F11%2F1238.atom) 15. 15.Gabrielson M, Azam S, Hardell E, Holm M, Ubhayasekera KA, Eriksson M, et al. Hormonal determinants of mammographic density and density change. Breast Cancer Res 2020; 22: 95. [CrossRef](http://smj.org.sa/lookup/external-ref?access_num=10.1186/s13058-020-01332-4&link_type=DOI) [PubMed](http://smj.org.sa/lookup/external-ref?access_num=32847607&link_type=MED&atom=%2Fsmj%2F45%2F11%2F1238.atom) 16. 16.Aloufi AS, AlNaeem A, Almousa A, Malik M, Hashem A, Altahan F, et al. Breast density distribution among the Saudi screening population and correlation between radiologist visual assessment and 2 automated methods. [Updated 2022; accessed 2024 Jun 25]. Available: [https://ui.adsabs.harvard.edu/abs/2022SPIE12035E..0LA/abstract](https://ui.adsabs.harvard.edu/abs/2022SPIE12035E..0LA/abstract) 17. 17.Albeshan SM, Hossain SZ, Mackey MG, Peat JK, Al Tahan FM, Brennan PC. Preliminary investigation of mammographic density among women in Riyadh: association with breast cancer risk factors and implications for screening practices. Clin Imaging 2019; 54: 138-147. [PubMed](http://smj.org.sa/lookup/external-ref?access_num=30639525&link_type=MED&atom=%2Fsmj%2F45%2F11%2F1238.atom) 18. 18.Albeshan SM, Hossain SZ, Mackey MG, Demchig D, Peat JK, Brennan PC. Mammographic density distribution in Ras Al Khaimah (RAK): relationships with demographic and reproductive factors. Asian Pac J Cancer Prev 2018; 19: 1607-1616. [PubMed](http://smj.org.sa/lookup/external-ref?access_num=29936786&link_type=MED&atom=%2Fsmj%2F45%2F11%2F1238.atom) 19. 19.Hudson S, Vik Hjerkind K, Vinnicombe S, Allen S, Trewin C, Ursin G, et al. Adjusting for BMI in analyses of volumetric mammographic density and breast cancer risk. Breast Cancer Res 2018; 20: 156. [PubMed](http://smj.org.sa/lookup/external-ref?access_num=30594212&link_type=MED&atom=%2Fsmj%2F45%2F11%2F1238.atom) 20. 20.Baglietto L, Krishnan K, Stone J, Apicella C, Southey MC, English DR, et al. Associations of mammographic dense and nondense areas and body mass index with risk of breast cancer. Am J Epidemiol 2014; 179: 475-483. [CrossRef](http://smj.org.sa/lookup/external-ref?access_num=10.1093/aje/kwt260&link_type=DOI) [PubMed](http://smj.org.sa/lookup/external-ref?access_num=24169466&link_type=MED&atom=%2Fsmj%2F45%2F11%2F1238.atom) [Web of Science](http://smj.org.sa/lookup/external-ref?access_num=000331264100010&link_type=ISI) 21. 21.Soguel L, Durocher F, Tchernof A, Diorio C. Adiposity, breast density, and breast cancer risk: epidemiological and biological considerations. Eur J Cancer Prev 2017; 26: 511-520. [PubMed](http://smj.org.sa/lookup/external-ref?access_num=27571214&link_type=MED&atom=%2Fsmj%2F45%2F11%2F1238.atom) 22. 22.Dabrosin N, Dabrosin C. Postmenopausal dense breasts maintain premenopausal levels of GH and insulin-like growth factor binding proteins in vivo. J Clin Endocrinol Metab 2020; 105: dgz323. [PubMed](http://smj.org.sa/lookup/external-ref?access_num=31900484&link_type=MED&atom=%2Fsmj%2F45%2F11%2F1238.atom)