Evaluation of the association of endometriosis and mammographic breast density, a cross-sectional study

Evaluation of the association of endometriosis and mammographic breast density, a cross-sectional study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Evaluation of the association of endometriosis and mammographic breast density, a cross-sectional study Ashraf Moini, Elnaz Salari, Hadi Rashidi, Khadije Maajani, Mahboubeh Abedi, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1007482/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 21 Mar, 2022 Read the published version in BMC Women's Health → Version 1 posted 10 You are reading this latest preprint version Abstract Background Endometriosis is a common benign but painful gynecologic condition. Studies suggest that the risk of some types of malignancies such as breast cancer is higher in women with endometriosis. Mammographic breast density (MBD) is known as an important predictor for breast cancer. The present study aimed to investigate the potential relationship between endometriosis and MBD. Methods This cross-sectional study was conducted on 370 women over 40 years of age. Laparoscopic surgery was carried out for the diagnosis of endometriosis. MBD was classified into four categories according to the ACR BI-RADS classification. Statistical analysis was performed using SPSS software to evaluate the potential association between variables. Results The mean age of all participants was 47.2±6.4 years, and most participants (76.8 %) were premenopausal. Multivariate analysis of the potential predictors of MBD, including age, body mass index, oral contraceptive consumption, progesterone consumption, family history of breast cancer and endometriosis showed that age (P-value=0.002), history of progesterone consumption (P-value=0.004) and endometriosis (P-value=0.006) were the independent factors for MBD. Conclusion This study indicated that endometriosis had an inverse association with MBD. Age and history of progesterone use were also independent influential factors for MBD. This finding shows that the positive association between breast cancer and endometriosis is not mediated through MBD. Internal Medicine Preventive Medicine Endometriosis Mammographic density Breast cancer Women Iran Introduction Endometriosis is a painful gynecologic condition defined by the presence of endometrial-like tissue outside the uterus [ 1 ]. As one of the most prevalent benign disorders of the female genital system, endometriosis is a debilitating disease with detrimental effects on social, occupational and psychological functioning. There are some similarities between endometriosis and female malignancies: progressive and invasive growth, estrogen-dependency, recurrence and tendency to metastasize [ 2 ]. According to the different epidemiological studies around the world, endometriosis affects about 10% of women at reproductive age and 30 to 50% of those who are suffering from chronic pelvic pain or infertility, which are the two major clinical symptoms of endometriosis [ 3 ]. It is known that sex steroid hormones have a key role in endometriosis development and progression [ 1 ]. Existing evidence suggests that the risk of some chronic diseases like cardiovascular disease, and some types of malignancies including ovarian and breast cancer might be higher in women with endometriosis [ 4 ]. Mammographic breast density (MBD), which indicates the fibro-glandular tissue content of the breast, is considered one of the important predictors for breast cancer among females in the general population. It has been shown that a high MBD (75% density) increases the risk of breast cancer by four-to-six folds in comparison to a low MBD (<5% density) [ 5 ]. It is assumed that exposure to sex-steroid hormones may have a role in MBD, particularly, menopausal hormone replacement therapy increases MBD, while menopausal status and tamoxifen decrease it [ 6 ]. As sex steroid exposure is associated with both endometriosis and MBD, and both are related with breast cancer, we aimed to investigate the potential relationship between endometriosis and MBD in women over 40 years of age. Methods This is a cross-sectional study carried out in Arash women’s hospital, Tehran, Iran. The study was approved by the Ethics Committee of Tehran University of Medical Sciences, Tehran, Iran (Approval ID: IR.TUMS.MEDICINE.REC.1398.130), and as a resident’s thesis by the Institutional Research Board of the University (Proposal Code: 961129000). All the protocols involving humans was in accordance to the institutional guidelines of Ethical Research of Tehran University of Medical Sciences and to the Declaration of Helsinki. Written informed consent was obtained from all participants. The study was conducted on women over 40 years of age. The estimated sample size was 180 for each group, calculated based on a prevalence of 40% for high MBD reported in the study of Alipour et al. [ 7 ], 95% confidence interval (CI), power of 80% and precision level of 5%. The final sample size was 360 plus 10 extra cases in the control group. Women who were diagnosed with endometriosis by laparoscopy were considered as cases, and controls were selected from women who had previously undergone laparoscopic surgery due to any reason (pelvic pain, dysmenorrhea, unknown infertility, etc.), and in whom the absence of endometriosis was confirmed during the surgery. Women who had undergone mammography less than one year sooner, those with a history of any type of cancer, positive genetic test for breast cancer (BRCA1, BRCA2), history of radiotherapy, and history of breast cancer in first degree relatives were excluded from the study. Data regarding demographic information and other risk factors including reproductive features were obtained through interview. Then, all eligible participants underwent mammography in our radiology center. MBD was classified into four categories and defined according to the American College of Radiology (ACR) Breast Imaging Reporting and Data System (BI-RADS) by two expert radiologists [ 8 ]. Data was analyzed using SPSS software Version 26. The continuous variables are reported as means ± SD, and numbers and percentages are used for reporting categorical variables. Normality for continuous variables was determined by the Kolmogorov-Smirnov test, which revealed the normal distribution of continuous variables ( P >0.05). The Independent T-Test, Pearson’s Chi-square and Fisher exact test were used for the comparison of differences between the variables in the study groups. Univariate and Multiple linear regression were applied to evaluate the possible association between endometriosis and potential risk factors. P values of <0.05 were accepted as significant. Results A total of 370 women were entered into the study. The mean age of all participants was 47.2 ±6.4; the youngest and oldest were 40 and 71 years old, respectively. Among all participants, 284 (76.8 %) were premenopausal and 86 (23.2 percent) women were postmenopausal. According to the analysis of demographic and clinical characteristics of participants, most of the variables were significantly different between the two groups, except for age at menarche, age at first pregnancy, duration of progesterone usage, history of infertility treatment, abortion, abdominal surgery and breast disease, which were not different between the two groups. The result are shown in Table 1. Univariate and multivariate analysis were carried out to understand the relative importance of potential predictors of MBD. Variables including age, body mass index (BMI), oral contraceptive (OCP) use, progesterone use, family history of breast cancer and endometriosis were included as independent predictors for MBD. Univariate analysis revealed that endometriosis (P-value=0.001), as well as age (P-value=0.001) were associate with MBD. Consequently, the potential factors were included in multivariate analysis, and the results showed that endometriosis (P-value=0.006), age (P-value=0.002), and history of progesterone consumption (P-value=0.004) were independent factors for MBD (Table 2). Table1. Demographic and clinical characteristics in the two study groups Characteristic Cases (N = 180) Controls (N = 190) P-value Age 44.51 ± 4.40 49.85 ± 6.99 0.001 Parity 1.61 ± 1.22 2.47 ± 1.37 <0.001 Gravidity 1.95 ± 1.40 2.8 ± 1.49 <0.001 BMI 27 ± 4.27 28.7 ±4.36 0.001 Age at menarche 13.41 ± 1.5 13.33 ± 1.20 0.57 Age at first pregnancy 22.45 ± 4.94 21.44 ±5.12 0.07 Menopause age 46.15 ± 4 49.34 ± 4.82 0.002 OCP usage duration (Year) 1.85 ± 2.43 3.51 ± 4.87 0.002 Progesterone usage duration (Year) 1.41 ± 2.46 1.50 ± 2.45 0.86 Lactation duration Never Less than 6 months 7-12 month 13-24 month More than 24 month 42(23.3%) 3(1.7%) 0(0%) 63(35%) 72(40%) 13(6.8%) 3(1.6%) 9(4.7%) 73(38.4%) 92(48.4%) 0.001 Menopausal status 28(15.4%) 58(30.5%) 0.001 Infertility 49(27.2%) 17(8.9%) 0.001 Infertility treatment (n=66) 33(67%) 9(52.9%) 0.28 History of miscarriage 50(27.8%) 59(31.1%) 0.49 History of curettage 20(11.1%) 31(16.3%) 0.14 OCP usage 114(63.3%) 101(53.2%) 0.047 Progesterone usage 93(51.7%) 34(17.9%) 0.001 Adenomyosis 27(15%) 14(7.4%) 0.01 Abdominal surgery 116(64%) 108(56.8%) 0.13 Dysmenorrhea 121(67.2%) 92(48.4%) 0.001 Dyspareunia 78(43.3%) 49(25.8%) 0.001 Pelvic pain 111(61.7%) 60(31.6%) 0.001 Breast disease 49(27.2%) 74(38.9%) 0.01 Type of breast disease Fibro adenoma (n=48) Fibrocystic disease (n=69) 1(2.1%) 47(97.9%) 4(5.8%) 65(94.2%) 0.32 Fist degree family history of breast cancer 16(8.9%) 33(17.4%) 0.01 Oophorectomy Unilateral Bilateral 4(2.2%) 10(5.6%) 1(0.5%) 0(0%) 0.01 Hysterectomy 16(8.9) 4(2.1) 0.004 Breast density Grade1 Grade2 Grade3 Grade4 113(62.8%) 56(31.1%) 11(6.1%) 0(0%) 77(40.5%) 96(50.5%) 13(6.8%) 4(2.1%) 0.001 Table2. Univariate and multivariate analysis for mammographic breast density Univariate linear regression Multivariate linear regression Variable Mean SD P-value Mean SD P-value Age 0.02 0.005 0.001 0.01 0.006 0.002 BMI 0.008 0.008 0.28 -0.001 0.008 0.88 OCP (No, Yes) -0.11 0.07 0.10 -0.06 0.06 0.35 Progesterone (No, Yes) 0.027 0.07 0.71 0.15 0.07 0.04 Family history of breast cancer 0.13 0.10 0.17 0.10 0.1 0.31 Endometriosis -0.27 0.06 0.001 -0.21 0.07 0.006 SD= Standard deviation Discussion In this study we evaluated the association between endometriosis and MBD, and the risk factors of endometriosis in the case and control groups. We found that women with endometriosis had a lower MBD than those without endometriosis. Age and progesterone usage were the other predictors of MBD. According to the studies around the world, the rate of diagnosing endometriosis is rising due to the increased awareness of women about the disease, changing social patterns like late marriage, and the widespread use of laparoscopy [ 9 ]. On the other hand, MBD is a potential risk factor for breast cancer. There are several studies that confirm the association between this cancer and MBD [ 10 – 13 ]. The risk of breast cancer according to MBD category varies by studies, a study reported that women with high MBD have two times a higher risk for this cancer [ 10 ]. Another study reported a four-to-six fold risk of breast cancer in women with high MBD [ 14 ]. What stands out from these reports is that MBD has a major impact on breast malignancy. Thus, investigating the influential factors on MBD can play a major role in prevention and control of breast cancer, also evaluation of a possible association between endometriosis and MBD may pave the way to revealing the pathway from endometriosis to breast cancer. To evaluate the role of endometriosis on MBD, we conducted Univariate and Multivariate linear regression analysis. In addition to endometriosis, age, BMI, OCP, progesterone use, and family history of breast cancer were expected to impact MBD based on previous knowledge; and were considered in the analysis. The result of Univariate analysis revealed that age and endometriosis were independently associated with MBD. Consequently, these factors were included in the multivariate analysis, and result showed that age, progesterone use and endometriosis were independently associated with MBD. According to the findings, endometriosis is a significant predictor for MBD; however in contrast with our expectation, MBD was lower in women with endometriosis. The mechanism for this reverse association is not clear to us, but this shows that the association of endometriosis and breast cancer is not through MBD. It also infers that sex hormones alone are not implicated in female cancers after endometriosis. To the best of our knowledge, the only study which evaluated the relationship between endometriosis and MBD was that of Farland et al [ 15 ]. According to this study, endometriosis was not found to be associated with mammographic density, which was in contrast with our finding. However, our sample size was higher, and Farland et al did not consider the use of steroid hormones as a confounding factor. Age and progesterone use were the other variables that showed significant relationship with MBD. We found that a history of progesterone consumption was associated with a higher MBD. There are studies that are in agreement with our finding about the role of progesterone in MBD [ 16 – 19 ]. Those studies also reported that higher levels of progesterone were associated with greater MBD. This finding is not unexpected, as progesterone has a key role in regulation of tissue development and maturation in the young breast, and atrophy and involution of the lobules and ducts during and after menopause [ 20 ]. Among variables that were evaluated as influential factors for MBD, BMI and OCP usage were not significantly associated with MBD. These variables have been reported as associated with MBD in some studies. For instance, in a study conducted by Yang et al, BMI was negatively correlated with MBD [ 21 ]. In a study conducted on Chinese women, Shang et al identified BMI as an independent influential factor on MBD [ 22 ]. In conclusion, our study showed that endometriosis was inversely associated with BMD. Considering the increased risk of breast cancer in women with higher BMD, our findings show that were there a positive association between endometriosis and breast cancer, this is not mediated via MBD. Further studies are warranted to define the complex relations among endometriosis, MBD and breast cancer. Declarations Ethics approval and consent to participate: The study was approved by the Ethics Committee of Tehran University of Medical Sciences, Tehran, Iran (Approval ID: IR.TUMS.MEDICINE.REC.1398.130). All the protocols involving humans was in accordance to the institutional guidelines of Ethical Research of Tehran University of Medical Sciences and to the Declaration of Helsinki. Written informed consent was obtained from all participants. Consent for publication: Not applicable. Availability of data and materials: Data are available per request from Elnaz Salari at [email protected] . Competing interests: Not applicable. Funding: Not applicable. Authors' contributions: AM: conception and design of the project, substantial revision of the manuscript, approval of the submitted manuscript. ES: conception and design of the project, interpretation of data, approval of the submitted manuscript. HR: analysis and interpretation of data, drafting the manuscript, approval of the submitted manuscript. KM: design of the project, analysis and interpretation of data, approval of the submitted manuscript. MA: design of the project, acquisition of data, approval of the submitted manuscript. LB: design of the project, acquisition of data, approval of the submitted manuscript. SA: design of the project, interpretation of data, substantial revision of the manuscript, approval of the submitted manuscript. Acknowledgements: We would like to acknowledge Ms. Matina Noori for her assistance in data gathering, as well as the staff of the Radiology Unit of Arash Women’s Hospital. References Farland LV, Tamimi RM, Eliassen AH, Spiegelman D, Hankinson SE, Chen WY,et al. Laparoscopically Confirmed Endometriosis and Breast Cancer in the Nurses' Health Study II. Obstet Gynecol. 2016; 128(5):1025-1031. Mehedintu C, Plotogea MN, Ionescu S, Antonovici M. Endometriosis still a challenge. J Med Life. 2014; 7(3):349-57. Wang Y, Nicholes K, Shih IM. The Origin and Pathogenesis of Endometriosis. Annu Rev Pathol. 2020; 15:71-95. Kvaskoff M, Mu F, Terry KL, et al. Endometriosis: a high-risk population for major chronic diseases? Hum Reprod Update. 2015; 21(4):500-16. Moran O, Eisen A, Demsky R, Blackmore K, Knight JA, Panchal S, et al. Predictors of mammographic density among women with a strong family history of breast cancer. BMC Cancer. 2019, 26; 19(1): 631. Gierach GL, Patel DA, Falk RT, Pfeiffer RM, Geller BM, Vacek PM, et al. Relationship of serum estrogens and metabolites with area and volume mammographic densities. Horm Cancer. 2015; 6(2-3):107-19. Eslami B, Alipour S, Khazaei N, Sepidarkish M, Moini A. Breast Cancer Risk Factors in Patients With Endometriosis. Arch Breast Cancer. 2018; 8:76-80 Price ER, Hargreaves J, Lipson JA, Sickles EA, Brenner RJ, Lindfors KK, et al. The California breast density information group: a collaborative response to the issues of breast density, breast cancer risk, and breast density notification legislation. Radiology. 2013; 269(3): 887-92. Okeke TC, Ikeako LC, Ezenyeaku CC. Endometriosis. Niger J Med. 2011; 20(2):191-9. Bertrand KA, Tamimi RM, Scott CG, Jensen MR, Pankratz V, Visscher D, et al. Mammographic density and risk of breast cancer by age and tumor characteristics. Breast Cancer Res. 2013; 15(6): R104. doi: 10.1186/bcr3570. Ding J, Warren R, Girling A, Thompson D, Easton D. Mammographic density, estrogen receptor status and other breast cancer tumor characteristics. Breast J. 2010; 16(3): 279-89. Lokate M, Stellato RK, Veldhuis WB, Peeters PH, van Gils CH. Age-related changes in mammographic density and breast cancer risk. Am J Epidemiol. 2013; 178(1): 101-9. Martin LJ, Melnichouk O, Guo H, Chiarelli AM, Hislop TG, Yaffe MJ, et al. Family history, mammographic density, and risk of breast cancer. Cancer Epidemiol Biomarkers Prev. 2010; 19 (2): 456-63. Maskarinec G, Woolcott CG, Kolonel LN. Mammographic density as a predictor of breast cancer outcome. Future Oncol. 2010; 6(3): 351-4. Farland LV, Tamimi RM, Eliassen AH, Spiegelman D, Bertrand KA, Missmer SA. Endometriosis and mammographic density measurements in the Nurses' Health Study II. Cancer Causes Control. 2016; 27(10): 1229-37. Bertrand KA, Eliassen AH, Hankinson SE, Rosner BA, Tamimi RM. Circulating hormones and mammographic density in premenopausal women. Hormones Cancer. 2018; 9(2):117–27. Noh JJ, Maskarinec G, Pagano I, Cheung LW, Stanczyk FZ. Mammographic densities and circulating hormones: a cross-sectional study in premenopausal women. Breast. 2006; 15(1):20–8. Iversen A, Frydenberg H, Furberg AS, Flote VG, Finstad SE, McTiernan A, et al. Cyclic endogenous estrogen and progesterone vary by mammographic density phenotypes in premenopausal women. Eur J Cancer Prev. 2016; 25(1):9–18. 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(1):95. Diep CH, Daniel AR, Mauro LJ, Knutson TP, Lange CA. Progesterone action in breast, uterine, and ovarian cancers. J Mol Endocrinol. 2015; 54(2):R31–53. doi: 10.1530/JME-14-0252 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. 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 (Baltimore). 2021; 100(28):e26586. doi: 10.1097/MD.0000000000026586. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 21 Mar, 2022 Read the published version in BMC Women's Health → Version 1 posted Editorial decision: Major revision 03 Mar, 2022 Reviews received at journal 04 Dec, 2021 Reviews received at journal 01 Dec, 2021 Reviewers agreed at journal 25 Nov, 2021 Reviewers agreed at journal 25 Nov, 2021 Reviewers invited by journal 25 Nov, 2021 Editor assigned by journal 25 Nov, 2021 Editor invited by journal 01 Nov, 2021 Submission checks completed at journal 01 Nov, 2021 First submitted to journal 22 Oct, 2021 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-1007482","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":60508959,"identity":"8802235a-e7cf-469a-9718-ebe232c1af78","order_by":0,"name":"Ashraf Moini","email":"","orcid":"","institution":"Tehran University of Medical Sciences","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Ashraf","middleName":"","lastName":"Moini","suffix":""},{"id":60508960,"identity":"a58c8e23-f2ef-4830-97cd-b2c8e3d5f8e8","order_by":1,"name":"Elnaz Salari","email":"","orcid":"","institution":"Tehran University of Medical Sciences","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Elnaz","middleName":"","lastName":"Salari","suffix":""},{"id":60508961,"identity":"e7c9bdcc-e04d-40d2-98e5-2f127d0ac2f2","order_by":2,"name":"Hadi Rashidi","email":"","orcid":"","institution":"Tehran University of Medical Sciences","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Hadi","middleName":"","lastName":"Rashidi","suffix":""},{"id":60508962,"identity":"bdff0176-59db-417d-be9d-523896110dcc","order_by":3,"name":"Khadije Maajani","email":"","orcid":"","institution":"Tehran University of Medical Sciences","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Khadije","middleName":"","lastName":"Maajani","suffix":""},{"id":60508964,"identity":"0806e4ac-be62-445f-ab05-826332743a7e","order_by":4,"name":"Mahboubeh Abedi","email":"","orcid":"","institution":"Tehran University of Medical Sciences","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Mahboubeh","middleName":"","lastName":"Abedi","suffix":""},{"id":60508966,"identity":"1d3152aa-502f-4530-b0a0-4a58ee484bc6","order_by":5,"name":"Leila Bayani","email":"","orcid":"","institution":"Tehran University of Medical Sciences","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Leila","middleName":"","lastName":"Bayani","suffix":""},{"id":60508968,"identity":"43e59516-ee7d-4036-9ad4-caa1a1acd272","order_by":6,"name":"Sadaf Alipour","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6ElEQVRIiWNgGAWjYHACA4YKBgseMPMDA0MCcVrOMEiAtTDOIEULmMXMQ4wW+fbDGz8cqJGQMWfvMfts22aXx8/ewPjhYw4eK86kFUscOCbBY9lzxnh2bltysWTPAWbJmdvwuSrHQPoDmwSPwY0cY+bcNubEDTcS2Jh58WiR739j/OPAP6gWy7Z6wloYbuSYSRxsg2phbDtMWIvBjWdlFgf7QH45VszYc+544syeg814/SLfn7z5xoFvNvbm7M2bGX6UVSf2szcf/PARn8Pg1oEIRjYw2UCEepgWhj/EKR4Fo2AUjIKRBQCjilHJ5gSMtgAAAABJRU5ErkJggg==","orcid":"","institution":"Tehran University of Medical Sciences","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Sadaf","middleName":"","lastName":"Alipour","suffix":""}],"badges":[],"createdAt":"2021-10-22 10:44:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-1007482/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-1007482/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12905-022-01663-8","type":"published","date":"2022-03-21T11:50:50+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":19426137,"identity":"d1e087aa-0c33-49ef-951a-4aa53375dfa0","added_by":"auto","created_at":"2022-03-21 11:50:56","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":264293,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1007482/v1/00faa1b3-f6b1-4cd8-923a-22efe135bd9b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eEvaluation of the association of endometriosis and mammographic breast density, a cross-sectional study\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eEndometriosis is a painful gynecologic condition defined by the presence of endometrial-like tissue outside the uterus [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. As one of the most prevalent benign disorders of the female genital system, endometriosis is a debilitating disease with detrimental effects on social, occupational and psychological functioning. There are some similarities between endometriosis and female malignancies: progressive and invasive growth, estrogen-dependency, recurrence and tendency to metastasize [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. According to the different epidemiological studies around the world, endometriosis affects about 10% of women at reproductive age and 30 to 50% of those who are suffering from chronic pelvic pain or infertility, which are the two major clinical symptoms of endometriosis [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. It is known that sex steroid hormones have a key role in endometriosis development and progression [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Existing evidence suggests that the risk of some chronic diseases like cardiovascular disease, and some types of malignancies including ovarian and breast cancer might be higher in women with endometriosis [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Mammographic breast density (MBD), which indicates the fibro-glandular tissue content of the breast, is considered one of the important predictors for breast cancer among females in the general population. It has been shown that a high MBD (75% density) increases the risk of breast cancer by four-to-six folds in comparison to a low MBD (\u0026lt;5% density) [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. It is assumed that exposure to sex-steroid hormones may have a role in MBD, particularly, menopausal hormone replacement therapy increases MBD, while menopausal status and tamoxifen decrease it [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAs sex steroid exposure is associated with both endometriosis and MBD, and both are related with breast cancer, we aimed to investigate the potential relationship between endometriosis and MBD in women over 40 years of age.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eThis is a cross-sectional study carried out in Arash women\u0026rsquo;s hospital, Tehran, Iran. The study was approved by the Ethics Committee of Tehran University of Medical Sciences, Tehran, Iran (Approval ID: IR.TUMS.MEDICINE.REC.1398.130), and as a resident\u0026rsquo;s thesis by the Institutional Research Board of the University (Proposal Code: 961129000). All the protocols involving humans was in accordance to the institutional guidelines of Ethical Research of Tehran University of Medical Sciences and to the Declaration of Helsinki. Written informed consent was obtained from all participants.\u003c/p\u003e \u003cp\u003eThe study was conducted on women over 40 years of age. The estimated sample size was 180 for each group, calculated based on a prevalence of 40% for high MBD reported in the study of Alipour et al. [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], 95% confidence interval (CI), power of 80% and precision level of 5%. The final sample size was 360 plus 10 extra cases in the control group. Women who were diagnosed with endometriosis by laparoscopy were considered as cases, and controls were selected from women who had previously undergone laparoscopic surgery due to any reason (pelvic pain, dysmenorrhea, unknown infertility, etc.), and in whom the absence of endometriosis was confirmed during the surgery. Women who had undergone mammography less than one year sooner, those with a history of any type of cancer, positive genetic test for breast cancer (BRCA1, BRCA2), history of radiotherapy, and history of breast cancer in first degree relatives were excluded from the study. Data regarding demographic information and other risk factors including reproductive features were obtained through interview. Then, all eligible participants underwent mammography in our radiology center. MBD was classified into four categories and defined according to the American College of Radiology (ACR) Breast Imaging Reporting and Data System (BI-RADS) by two expert radiologists [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Data was analyzed using SPSS software Version 26.\u003c/p\u003e \u003cp\u003eThe continuous variables are reported as means \u0026plusmn; SD, and numbers and percentages are used for reporting categorical variables. Normality for continuous variables was determined by the Kolmogorov-Smirnov test, which revealed the normal distribution of continuous variables (\u003cem\u003eP\u003c/em\u003e\u0026gt;0.05). The Independent T-Test, Pearson\u0026rsquo;s Chi-square and Fisher exact test were used for the comparison of differences between the variables in the study groups. Univariate and Multiple linear regression were applied to evaluate the possible association between endometriosis and potential risk factors. \u003cem\u003eP\u003c/em\u003e values of \u0026lt;0.05 were accepted as significant.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eA total of 370 women were entered into the study. The mean age of all participants was 47.2 \u0026plusmn;6.4; the youngest and oldest were 40 and 71 years old, respectively. Among all participants, 284 (76.8 %) were premenopausal and 86 (23.2 percent) women were postmenopausal.\u003c/p\u003e \u003cp\u003eAccording to the analysis of demographic and clinical characteristics of participants, most of the variables were significantly different between the two groups, except for age at menarche, age at first pregnancy, duration of progesterone usage, history of infertility treatment, abortion, abdominal surgery and breast disease, which were not different between the two groups. The result are shown in Table 1.\u003c/p\u003e \u003cp\u003eUnivariate and multivariate analysis were carried out to understand the relative importance of potential predictors of MBD. Variables including age, body mass index (BMI), oral contraceptive (OCP) use, progesterone use, family history of breast cancer and endometriosis were included as independent predictors for MBD. Univariate analysis revealed that endometriosis (P-value=0.001), as well as age (P-value=0.001) were associate with MBD. Consequently, the potential factors were included in multivariate analysis, and the results showed that endometriosis (P-value=0.006), age (P-value=0.002), and history of progesterone consumption (P-value=0.004) were independent factors for MBD (Table 2).\u003c/p\u003e \u003cp\u003eTable1. Demographic and clinical characteristics in the two study groups\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"4\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCases (N = 180)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eControls (N = 190)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44.51 \u0026plusmn; 4.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49.85 \u0026plusmn; 6.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.61 \u0026plusmn; 1.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.47 \u0026plusmn; 1.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGravidity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.95 \u0026plusmn; 1.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.8 \u0026plusmn; 1.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27 \u0026plusmn; 4.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28.7 \u0026plusmn;4.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge at menarche\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13.41 \u0026plusmn; 1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.33 \u0026plusmn; 1.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.57\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge at first pregnancy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22.45 \u0026plusmn; 4.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21.44 \u0026plusmn;5.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMenopause age\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46.15 \u0026plusmn; 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49.34 \u0026plusmn; 4.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOCP usage duration (Year)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.85 \u0026plusmn; 2.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.51 \u0026plusmn; 4.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProgesterone usage duration\u003c/p\u003e \u003cp\u003e(Year)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.41 \u0026plusmn; 2.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.50 \u0026plusmn; 2.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLactation duration\u003c/p\u003e \u003cp\u003eNever\u003c/p\u003e \u003cp\u003eLess than 6 months\u003c/p\u003e \u003cp\u003e7-12 month\u003c/p\u003e \u003cp\u003e13-24 month\u003c/p\u003e \u003cp\u003eMore than 24 month\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e42(23.3%)\u003c/p\u003e \u003cp\u003e3(1.7%)\u003c/p\u003e \u003cp\u003e0(0%)\u003c/p\u003e \u003cp\u003e63(35%)\u003c/p\u003e \u003cp\u003e72(40%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13(6.8%)\u003c/p\u003e \u003cp\u003e3(1.6%)\u003c/p\u003e \u003cp\u003e9(4.7%)\u003c/p\u003e \u003cp\u003e73(38.4%)\u003c/p\u003e \u003cp\u003e92(48.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMenopausal status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28(15.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e58(30.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInfertility\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e49(27.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17(8.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInfertility treatment (n=66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33(67%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9(52.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistory of miscarriage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50(27.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59(31.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistory of curettage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20(11.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31(16.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOCP usage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e114(63.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e101(53.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.047\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProgesterone usage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e93(51.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34(17.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdenomyosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27(15%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14(7.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbdominal surgery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e116(64%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e108(56.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDysmenorrhea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e121(67.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e92(48.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDyspareunia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e78(43.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49(25.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePelvic pain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e111(61.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60(31.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBreast disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e49(27.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e74(38.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eType of breast disease\u003c/p\u003e \u003cp\u003eFibro adenoma (n=48)\u003c/p\u003e \u003cp\u003eFibrocystic disease (n=69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1(2.1%)\u003c/p\u003e \u003cp\u003e47(97.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4(5.8%)\u003c/p\u003e \u003cp\u003e65(94.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFist degree family history of breast cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16(8.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33(17.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOophorectomy\u003c/p\u003e \u003cp\u003eUnilateral\u003c/p\u003e \u003cp\u003eBilateral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4(2.2%)\u003c/p\u003e \u003cp\u003e10(5.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1(0.5%)\u003c/p\u003e \u003cp\u003e0(0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHysterectomy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16(8.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4(2.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBreast density\u003c/p\u003e \u003cp\u003eGrade1\u003c/p\u003e \u003cp\u003eGrade2\u003c/p\u003e \u003cp\u003eGrade3\u003c/p\u003e \u003cp\u003eGrade4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e113(62.8%)\u003c/p\u003e \u003cp\u003e56(31.1%)\u003c/p\u003e \u003cp\u003e11(6.1%)\u003c/p\u003e \u003cp\u003e0(0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e77(40.5%)\u003c/p\u003e \u003cp\u003e96(50.5%)\u003c/p\u003e \u003cp\u003e13(6.8%)\u003c/p\u003e \u003cp\u003e4(2.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable2. Univariate and multivariate analysis for mammographic breast density\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabb\" border=\"1\"\u003e \u003ccolgroup cols=\"7\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eUnivariate linear regression\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eMultivariate linear regression\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOCP\u003c/p\u003e \u003cp\u003e(No, Yes)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProgesterone\u003c/p\u003e \u003cp\u003e(No, Yes)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFamily history of breast cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEndometriosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eSD= Standard deviation\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study we evaluated the association between endometriosis and MBD, and the risk factors of endometriosis in the case and control groups. We found that women with endometriosis had a lower MBD than those without endometriosis. Age and progesterone usage were the other predictors of MBD.\u003c/p\u003e \u003cp\u003eAccording to the studies around the world, the rate of diagnosing endometriosis is rising due to the increased awareness of women about the disease, changing social patterns like late marriage, and the widespread use of laparoscopy [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOn the other hand, MBD is a potential risk factor for breast cancer. There are several studies that confirm the association between this cancer and MBD [\u003cspan additionalcitationids=\"CR11 CR12\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The risk of breast cancer according to MBD category varies by studies, a study reported that women with high MBD have two times a higher risk for this cancer [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Another study reported a four-to-six fold risk of breast cancer in women with high MBD [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. What stands out from these reports is that MBD has a major impact on breast malignancy. Thus, investigating the influential factors on MBD can play a major role in prevention and control of breast cancer, also evaluation of a possible association between endometriosis and MBD may pave the way to revealing the pathway from endometriosis to breast cancer.\u003c/p\u003e \u003cp\u003eTo evaluate the role of endometriosis on MBD, we conducted Univariate and Multivariate linear regression analysis. In addition to endometriosis, age, BMI, OCP, progesterone use, and family history of breast cancer were expected to impact MBD based on previous knowledge; and were considered in the analysis. The result of Univariate analysis revealed that age and endometriosis were independently associated with MBD. Consequently, these factors were included in the multivariate analysis, and result showed that age, progesterone use and endometriosis were independently associated with MBD.\u003c/p\u003e \u003cp\u003eAccording to the findings, endometriosis is a significant predictor for MBD; however in contrast with our expectation, MBD was lower in women with endometriosis. The mechanism for this reverse association is not clear to us, but this shows that the association of endometriosis and breast cancer is not through MBD. It also infers that sex hormones alone are not implicated in female cancers after endometriosis. To the best of our knowledge, the only study which evaluated the relationship between endometriosis and MBD was that of Farland et al [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. According to this study, endometriosis was not found to be associated with mammographic density, which was in contrast with our finding. However, our sample size was higher, and Farland et al did not consider the use of steroid hormones as a confounding factor.\u003c/p\u003e \u003cp\u003eAge and progesterone use were the other variables that showed significant relationship with MBD. We found that a history of progesterone consumption was associated with a higher MBD. There are studies that are in agreement with our finding about the role of progesterone in MBD [\u003cspan additionalcitationids=\"CR17 CR18\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Those studies also reported that higher levels of progesterone were associated with greater MBD. This finding is not unexpected, as progesterone has a key role in regulation of tissue development and maturation in the young breast, and atrophy and involution of the lobules and ducts during and after menopause [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAmong variables that were evaluated as influential factors for MBD, BMI and OCP usage were not significantly associated with MBD. These variables have been reported as associated with MBD in some studies. For instance, in a study conducted by Yang et al, BMI was negatively correlated with MBD [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. In a study conducted on Chinese women, Shang et al identified BMI as an independent influential factor on MBD [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn conclusion, our study showed that endometriosis was inversely associated with BMD. Considering the increased risk of breast cancer in women with higher BMD, our findings show that were there a positive association between endometriosis and breast cancer, this is not mediated via MBD. Further studies are warranted to define the complex relations among endometriosis, MBD and breast cancer.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eEthics approval and consent to participate:\u003c/em\u003e\u003c/strong\u003e The study was approved by the Ethics Committee of Tehran University of Medical Sciences, Tehran, Iran (Approval ID: IR.TUMS.MEDICINE.REC.1398.130). All the protocols involving humans was in accordance to the institutional guidelines of Ethical Research of Tehran University of Medical Sciences and to the Declaration of Helsinki. Written informed consent was obtained from all participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u003c/strong\u003e Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials:\u003c/strong\u003e Data are available per request from Elnaz Salari at [email protected].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u003c/strong\u003e Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions:\u003c/strong\u003e AM: conception and design of the project, substantial revision of the manuscript, approval of the submitted manuscript. ES: conception and design of the project, interpretation of data, approval of the submitted manuscript. HR: analysis and interpretation of data, drafting the manuscript, approval of the submitted manuscript. KM: design of the project, analysis and interpretation of data, approval of the submitted manuscript. MA: design of the project, acquisition of data, approval of the submitted manuscript. LB: design of the project, acquisition of data, approval of the submitted manuscript. SA: design of the project, interpretation of data, substantial revision of the manuscript, approval of the submitted manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u0026nbsp;\u003c/strong\u003eWe would like to acknowledge Ms. Matina Noori for her assistance in data gathering, as well as the staff of the Radiology Unit of Arash Women\u0026rsquo;s Hospital.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eFarland LV, Tamimi RM, Eliassen AH, Spiegelman D, Hankinson SE, Chen WY,et al. Laparoscopically Confirmed Endometriosis and Breast Cancer in the Nurses\u0026apos; Health Study II. Obstet Gynecol. 2016; 128(5):1025-1031.\u003c/li\u003e\n \u003cli\u003eMehedintu C, Plotogea MN, Ionescu S, Antonovici M. Endometriosis still a challenge. J Med Life. 2014; 7(3):349-57.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eWang Y, Nicholes K, Shih IM. The Origin and Pathogenesis of Endometriosis. Annu Rev Pathol. 2020; 15:71-95.\u003c/li\u003e\n \u003cli\u003eKvaskoff M, Mu F, Terry KL, et al. Endometriosis: a high-risk population for major chronic diseases? Hum Reprod Update. 2015; 21(4):500-16.\u003c/li\u003e\n \u003cli\u003eMoran O, Eisen A, Demsky R, Blackmore K, Knight JA, Panchal S, et al. Predictors of mammographic density among women with a strong family history of breast cancer. BMC Cancer. 2019, 26; 19(1): 631.\u003c/li\u003e\n \u003cli\u003eGierach GL, Patel DA, Falk RT, Pfeiffer RM, Geller BM, Vacek PM, et al. Relationship of serum estrogens and metabolites with area and volume mammographic densities. Horm Cancer. 2015; 6(2-3):107-19.\u003c/li\u003e\n \u003cli\u003eEslami B, Alipour S, Khazaei N, Sepidarkish M, Moini A. Breast Cancer Risk Factors in Patients With Endometriosis. Arch Breast Cancer. 2018; 8:76-80\u003c/li\u003e\n \u003cli\u003ePrice ER, Hargreaves J, Lipson JA, Sickles EA, Brenner RJ, Lindfors KK, et al. The California breast density information group: a collaborative response to the issues of breast density, breast cancer risk, and breast density notification legislation. Radiology. 2013; 269(3): 887-92.\u003c/li\u003e\n \u003cli\u003eOkeke TC, Ikeako LC, Ezenyeaku CC. Endometriosis. Niger J Med. 2011; 20(2):191-9.\u003c/li\u003e\n \u003cli\u003eBertrand KA, Tamimi RM, Scott CG, Jensen MR, Pankratz V, Visscher D, et al. Mammographic density and risk of breast cancer by age and tumor characteristics. Breast Cancer Res. 2013; 15(6): R104. doi: 10.1186/bcr3570.\u003c/li\u003e\n \u003cli\u003eDing J, Warren R, Girling A, Thompson D, Easton D. Mammographic density, estrogen receptor status and other breast cancer tumor characteristics. Breast J. 2010; 16(3): 279-89.\u003c/li\u003e\n \u003cli\u003eLokate M, Stellato RK, Veldhuis WB, Peeters PH, van Gils CH. Age-related changes in mammographic density and breast cancer risk. Am J Epidemiol. 2013; 178(1): 101-9.\u003c/li\u003e\n \u003cli\u003eMartin LJ, Melnichouk O, Guo H, Chiarelli AM, Hislop TG, Yaffe MJ, et al. Family history, mammographic density, and risk of breast cancer. Cancer Epidemiol Biomarkers Prev. 2010; 19 (2): 456-63.\u003c/li\u003e\n \u003cli\u003eMaskarinec G, Woolcott CG, Kolonel LN. Mammographic density as a predictor of breast cancer outcome. Future Oncol. 2010; 6(3): 351-4.\u003c/li\u003e\n \u003cli\u003eFarland LV, Tamimi RM, Eliassen AH, Spiegelman D, Bertrand KA, Missmer SA. Endometriosis and mammographic density measurements in the Nurses\u0026apos; Health Study II. Cancer Causes Control. 2016; 27(10): 1229-37.\u003c/li\u003e\n \u003cli\u003eBertrand KA, Eliassen AH, Hankinson SE, Rosner BA, Tamimi RM. Circulating hormones and mammographic density in premenopausal women. Hormones Cancer. 2018; 9(2):117\u0026ndash;27.\u003c/li\u003e\n \u003cli\u003eNoh JJ, Maskarinec G, Pagano I, Cheung LW, Stanczyk FZ. Mammographic densities and circulating hormones: a cross-sectional study in premenopausal women. Breast. 2006; 15(1):20\u0026ndash;8.\u003c/li\u003e\n \u003cli\u003eIversen A, Frydenberg H, Furberg AS, Flote VG, Finstad SE, McTiernan A, et al. Cyclic endogenous estrogen and progesterone vary by mammographic density phenotypes in premenopausal women. Eur J Cancer Prev. 2016; 25(1):9\u0026ndash;18.\u003c/li\u003e\n \u003cli\u003eGabrielson 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(1):95.\u003c/li\u003e\n \u003cli\u003eDiep CH, Daniel AR, Mauro LJ, Knutson TP, Lange CA. Progesterone action in breast, uterine, and ovarian cancers. J Mol Endocrinol. 2015; 54(2):R31\u0026ndash;53. doi: 10.1530/JME-14-0252\u003c/li\u003e\n \u003cli\u003eYang 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.\u003c/li\u003e\n \u003cli\u003eShang 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 (Baltimore). 2021; 100(28):e26586. doi: 10.1097/MD.0000000000026586. \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-womens-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmwh","sideBox":"Learn more about [BMC Women's Health](http://bmcwomenshealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bmwh/default.aspx","title":"BMC Women's Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Endometriosis, Mammographic density, Breast cancer, Women, Iran ","lastPublishedDoi":"10.21203/rs.3.rs-1007482/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1007482/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eEndometriosis is a common benign but painful gynecologic condition. Studies suggest that the risk of some types of malignancies such as breast cancer is higher in women with endometriosis. Mammographic breast density (MBD) is known as an important predictor for breast cancer. The present study aimed to investigate the potential relationship between endometriosis and MBD.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis cross-sectional study was conducted on 370 women over 40 years of age. Laparoscopic surgery was carried out for the diagnosis of endometriosis. MBD was classified into four categories according to the ACR BI-RADS classification. Statistical analysis was performed using SPSS software to evaluate the potential association between variables.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe mean age of all participants was 47.2\u0026plusmn;6.4 years, and most participants (76.8 %) were premenopausal. Multivariate analysis of the potential predictors of MBD, including age, body mass index, oral contraceptive consumption, progesterone consumption, family history of breast cancer and endometriosis showed that age (P-value=0.002), history of progesterone consumption (P-value=0.004) and endometriosis (P-value=0.006) were the independent factors for MBD.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThis study indicated that endometriosis had an inverse association with MBD. Age and history of progesterone use were also independent influential factors for MBD. This finding shows that the positive association between breast cancer and endometriosis is not mediated through MBD.\u003c/p\u003e","manuscriptTitle":"Evaluation of the association of endometriosis and mammographic breast density, a cross-sectional study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2021-11-03 16:56:36","doi":"10.21203/rs.3.rs-1007482/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revision","date":"2022-03-03T12:01:57+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2021-12-04T18:58:34+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2021-12-01T11:07:57+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"5219b938-e2b6-4e1a-b90e-c84d5f334653","date":"2021-11-25T19:40:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"78a60429-df39-4ea2-9cff-d5fcf0b4d145","date":"2021-11-25T19:26:18+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2021-11-25T18:40:28+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2021-11-25T18:39:11+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2021-11-01T12:40:19+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2021-11-01T12:35:41+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Women's Health","date":"2021-10-22T10:43:01+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-womens-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmwh","sideBox":"Learn more about [BMC Women's Health](http://bmcwomenshealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bmwh/default.aspx","title":"BMC Women's Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"bb6a8e7b-b4b5-43ad-92e8-b0c14d17ea1e","owner":[],"postedDate":"November 3rd, 2021","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":8280472,"name":"Internal Medicine"},{"id":8280473,"name":"Preventive Medicine"}],"tags":[],"updatedAt":"2022-03-21T11:50:50+00:00","versionOfRecord":{"articleIdentity":"rs-1007482","link":"https://doi.org/10.1186/s12905-022-01663-8","journal":{"identity":"bmc-womens-health","isVorOnly":false,"title":"BMC Women's Health"},"publishedOn":"2022-03-21 11:50:50","publishedOnDateReadable":"March 21st, 2022"},"versionCreatedAt":"2021-11-03 16:56:36","video":"","vorDoi":"10.1186/s12905-022-01663-8","vorDoiUrl":"https://doi.org/10.1186/s12905-022-01663-8","workflowStages":[]},"version":"v1","identity":"rs-1007482","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-1007482","identity":"rs-1007482","version":["v1"]},"buildId":"FbvkV6FR0MCFSLy54lSbu","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}