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O'Driscoll J, Burton A, Maskarinec G, Perez-Gomez B, Vachon C, Miao H, Lajous M, López-Ridaura R, Eliassen AH, Pereira A, Garmendia ML, Tamimi RM, Bertrand K, Kwong A, Ursin G, Lee E, Qureshi SA, Ma H, Vinnicombe S, Moss S, Allen S, Ndumia R, Vinayak S, Teo SH, Mariapun S, Fadzli F, Peplonska B, Nagata C, Stone J, Hopper JL, Giles G, Ozmen V, Aribal ME, Schüz J, Van Gils CH, Wanders JOP, Sirous R, Sirous M, Hipwell J, Kim J, Lee JW, Hartman M, Li J, Scott C, Chiarelli AM, Linton L, Pollan M, Flugelman AA, Salem D, Kamal R, Boyd N, Dos-Santos-Silva I, McCormack V, Mullooly M. Reproductive factors and mammographic density within the International Consortium of Mammographic Density: A cross-sectional study. Breast Cancer Res 2024; 26:139. [PMID: 39350230 PMCID: PMC11443712 DOI: 10.1186/s13058-024-01890-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2024] [Accepted: 09/05/2024] [Indexed: 10/04/2024] Open
Abstract
BACKGROUND Elevated mammographic density (MD) for a woman's age and body mass index (BMI) is an established breast cancer risk factor. The relationship of parity, age at first birth, and breastfeeding with MD is less clear. We examined the associations of these factors with MD within the International Consortium of Mammographic Density (ICMD). METHODS ICMD is a consortium of 27 studies with pooled individual-level epidemiological and MD data from 11,755 women without breast cancer aged 35-85 years from 22 countries, capturing 40 country-& ethnicity-specific population groups. MD was measured using the area-based tool Cumulus. Meta-analyses across population groups and pooled analyses were used to examine linear regression associations of square-root (√) transformed MD measures (percent MD (PMD), dense area (DA), and non-dense area (NDA)) with parity, age at first birth, ever/never breastfed and lifetime breastfeeding duration. Models were adjusted for age at mammogram, age at menarche, BMI, menopausal status, use of hormone replacement therapy, calibration method, mammogram view and reader, and parity and age at first birth when not the association of interest. RESULTS Among 10,988 women included in these analyses, 90.1% (n = 9,895) were parous, of whom 13% (n = 1,286) had ≥ five births. The mean age at first birth was 24.3 years (Standard deviation = 5.1). Increasing parity (per birth) was inversely associated with √PMD (β: - 0.05, 95% confidence interval (CI): - 0.07, - 0.03) and √DA (β: - 0.08, 95% CI: - 0.12, - 0.05) with this trend evident until at least nine births. Women who were older at first birth (per five-year increase) had higher √PMD (β:0.06, 95% CI:0.03, 0.10) and √DA (β:0.06, 95% CI:0.02, 0.10), and lower √NDA (β: - 0.06, 95% CI: - 0.11, - 0.01). In stratified analyses, this association was only evident in women who were post-menopausal at MD assessment. Among parous women, no associations were found between ever/never breastfed or lifetime breastfeeding duration (per six-month increase) and √MD. CONCLUSIONS Associations with higher parity and older age at first birth with √MD were consistent with the direction of their respective associations with breast cancer risk. Further research is needed to understand reproductive factor-related differences in the composition of breast tissue and their associations with breast cancer risk.
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Affiliation(s)
- Jessica O'Driscoll
- School of Population Health, RCSI University of Medicine and Health Sciences, Beaux Lane House, Mercer Street Lower, Dublin 2, Ireland.
| | - Anya Burton
- Bristol Medical School, Translational Health Sciences, University of Bristol, Learning and Research Building, Level 2, Southmead Hospital, Bristol, UK
- Environment and Lifestyle Epidemiology Branch, International Agency for Research on Cancer, Lyon, France
| | | | | | - Celine Vachon
- Department of Quantitative Health Sciences, Division of Epidemiology, Mayo Clinic, Rochester, MN, USA
| | - Hui Miao
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore City, Singapore
| | - Martín Lajous
- Instituto Nacional de Salud Pública, Cuernavaca, Mexico
| | | | - A Heather Eliassen
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Departments of Nutrition and Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Ana Pereira
- Instituto de Nutrición y Tecnología de los Alimentos, Universidad de Chile, Santiago, Chile
| | - Maria Luisa Garmendia
- Instituto de Nutrición y Tecnología de los Alimentos, Universidad de Chile, Santiago, Chile
| | - Rulla M Tamimi
- Population Health Sciences, Weill Cornell Medicine, New York, NY, USA
| | | | - Ava Kwong
- Division of Breast Surgery, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong, China
- Department of Surgery and Cancer Genetics Center, Hong Kong Sanatorium and Hospital, Hong Kong, China
- Hong Kong Hereditary Breast Cancer Family Registry, Hong Kong, China
| | - Giske Ursin
- Cancer Registry of Norway, Norwegian Institute of Public Health, Oslo, Norway
- Department of Nutrition, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, USA
| | - Eunjung Lee
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, USA
| | - Samera A Qureshi
- Unit for Migration & Health, Norwegian Institute of Public Health, Oslo, Norway
| | - Huiyan Ma
- Department of Computational and Quantitative Medicine, City of Hope National Medical Center, Duarte, CA, USA
| | - Sarah Vinnicombe
- Division of Cancer Research, Ninewells Hospital and Medical School, Dundee, UK
| | - Sue Moss
- Wolfson Institute of Preventive Medicine, Queen Mary University of London, London, UK
| | - Steve Allen
- Department of Diagnostic Radiology, Royal Marsden NHS Foundation Trust, London, UK
| | - Rose Ndumia
- Aga Khan University Hospital, Nairobi, Kenya
| | | | - Soo-Hwang Teo
- Breast Cancer Research Group, University of Malaya Medical Centre, University of Malaya, Kuala Lumpur, Malaysia
- Cancer Research Malaysia, Subang Jaya, Malaysia
| | | | - Farhana Fadzli
- Breast Cancer Research Unit, Faculty of Medicine, University of Malaya Cancer Research Institute, University of Malaya, Kuala Lumpur, Malaysia
- Biomedical Imaging Department, University of Malaya Medical Centre, University of Malaya, Kuala Lumpur, Malaysia
| | - Beata Peplonska
- Department of Environmental Epidemiology, Nofer Institute of Occupational Medicine, Łódź, Poland
| | - Chisato Nagata
- Department of Epidemiology & Preventive Medicine, Graduate School of Medicine, Gifu University, Gifu, Japan
| | - Jennifer Stone
- Genetic Epidemiology Group, School of Population and Global Health, University of Western Australia, Perth, WA, Australia
| | - John L Hopper
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, VIC, Australia
| | - Graham Giles
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, VIC, Australia
- Cancer Epidemiology Centre, Cancer Council Victoria, Melbourne, VIC, Australia
| | - Vahit Ozmen
- Faculty of Medicine, Istanbul University, Istanbul, Turkey
| | - Mustafa Erkin Aribal
- Department of Radiology, School of Medicine, Acibadem University, Istanbul, Turkey
| | - Joachim Schüz
- Environment and Lifestyle Epidemiology Branch, International Agency for Research on Cancer, Lyon, France
| | - Carla H Van Gils
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Johanna O P Wanders
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Reza Sirous
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, USA
- Radiology Department, George Washington University Hospital, Washington, DC, USA
| | - Mehri Sirous
- Radiology Department, Isfahan University of Medical Sciences, Isfahan, Iran
| | - John Hipwell
- Centre for Medical Image Computing, University College London, London, UK
| | - Jisun Kim
- Asan Medical Center, Seoul, Republic of Korea
| | | | - Mikael Hartman
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore City, Singapore
- Department of Surgery, Yong Loo Lin School of Medicine, National University of Singapore, Singapore City, Singapore
- Department of Surgery, University Surgical Cluster, National University Hospital, Singapore City, Singapore
| | - Jingmei Li
- Department of Surgery, Yong Loo Lin School of Medicine, National University of Singapore, Singapore City, Singapore
- Genome Institute of Singapore, Agency for Science, Technology and Research (A*STAR), Singapore City, Singapore
| | - Christopher Scott
- Department of Quantitative Health Sciences, Division of Epidemiology, Mayo Clinic, Rochester, MN, USA
| | - Anna M Chiarelli
- Ontario Breast Screening Program, Cancer Care Ontario, Toronto, ON, Canada
| | - Linda Linton
- Princess Margaret Cancer Centre, Toronto, ON, Canada
| | - Marina Pollan
- Instituto de Salud Carlos III, Madrid, Spain
- CIBERESP, Madrid, Spain
| | - Anath Arzee Flugelman
- The Rapport Faculty of Medicine, Technion-Israel Institute of Technology, Haifa, Israel
- Samuel Neaman Institute for National Policy Research, Technion-Israel Institute of Technology, Haifa, Israel
| | - Dorria Salem
- Woman Imaging Unit, Radiodiagnosis Department, Kasr El Aini, Cairo University Hospitals, Cairo, Egypt
| | - Rasha Kamal
- Woman Imaging Unit, Radiodiagnosis Department, Kasr El Aini, Cairo University Hospitals, Cairo, Egypt
| | - Norman Boyd
- Princess Margaret Cancer Centre, Toronto, ON, Canada
| | - Isabel Dos-Santos-Silva
- Woman Imaging Unit, Radiodiagnosis Department, Kasr El Aini, Cairo University Hospitals, Cairo, Egypt
- Department of Non-Communicable Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
| | - Valerie McCormack
- Environment and Lifestyle Epidemiology Branch, International Agency for Research on Cancer, Lyon, France
| | - Maeve Mullooly
- School of Population Health, RCSI University of Medicine and Health Sciences, Beaux Lane House, Mercer Street Lower, Dublin 2, Ireland
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Ye DM, Bai X, Xu S, Qu N, Zhao N, Zheng Y, Yu T, Wu H. Association between breastfeeding, mammographic density, and breast cancer risk: a review. Int Breastfeed J 2024; 19:65. [PMID: 39285438 PMCID: PMC11406879 DOI: 10.1186/s13006-024-00672-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/15/2024] [Accepted: 09/07/2024] [Indexed: 09/19/2024] Open
Abstract
BACKGROUND Mammographic density has been associated with breast cancer risk, and is modulated by established breast cancer risk factors, such as reproductive and hormonal history, as well as lifestyle. Recent epidemiological and biological findings underscore the recognized benefits of breastfeeding in reducing breast cancer risk, especially for aggressive subtypes. Current research exploring the association among mammographic density, breastfeeding, and breast cancer is sparse. MAIN FINDINGS Changes occur in the breasts during pregnancy in preparation for lactation, characterized by the proliferation of mammary gland tissues and the development of mammary alveoli. During lactation, the alveoli fill with milk, and subsequent weaning triggers the involution and remodeling of these tissues. Breastfeeding influences the breast microenvironment, potentially altering mammographic density. When breastfeeding is not initiated after birth, or is abruptly discontinued shortly after, the breast tissue undergoes forced and abrupt involution. Conversely, when breastfeeding is sustained over an extended period and concludes gradually, the breast tissue undergoes slow remodeling process known as gradual involution. Breast tissue undergoing abrupt involution displays denser stroma, altered collagen composition, heightened inflammation and proliferation, along with increased expression of estrogen receptor α (ERα) and progesterone receptor. Furthermore, elevated levels of pregnancy-associated plasma protein-A (PAPP-A) surpass those of its inhibitors during abrupt involution, enhancing insulin-like growth factor (IGF) signaling and collagen deposition. Prolactin and small molecules in breast milk may also modulate DNA methylation levels. Drawing insights from contemporary epidemiological and molecular biology studies, our review sheds light on how breastfeeding impacts mammographic density and explores its role in influencing breast cancer. CONCLUSION This review highlights a clear protective link between breastfeeding and reduced breast cancer risk via changes in mammographic density. Future research should investigate the effects of breastfeeding on mammographic density and breast cancer risk among various ethnic groups and elucidate the molecular mechanisms underlying these associations. Such comprehensive research will enhance our understanding and facilitate the development of targeted breast cancer prevention and treatment strategies.
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Affiliation(s)
- Dong-Man Ye
- Department of Medical Imaging, Cancer Hospital of Dalian University of Technology, Liaoning Cancer Hospital & Institute, No.44 Xiaoheyan Road, Dadong District, Shenyang, 110042, China
| | - Xiaoru Bai
- Department of Medical Imaging, Cancer Hospital of Dalian University of Technology, Liaoning Cancer Hospital & Institute, No.44 Xiaoheyan Road, Dadong District, Shenyang, 110042, China
| | - Shu Xu
- Department of Medical Imaging, Cancer Hospital of Dalian University of Technology, Liaoning Cancer Hospital & Institute, No.44 Xiaoheyan Road, Dadong District, Shenyang, 110042, China
| | - Ning Qu
- Department of Medical Imaging, Cancer Hospital of Dalian University of Technology, Liaoning Cancer Hospital & Institute, No.44 Xiaoheyan Road, Dadong District, Shenyang, 110042, China
| | - Nannan Zhao
- Department of Medical Imaging, Cancer Hospital of Dalian University of Technology, Liaoning Cancer Hospital & Institute, No.44 Xiaoheyan Road, Dadong District, Shenyang, 110042, China
| | - Yang Zheng
- Department of Laboratory Medicine, Cancer Hospital of Dalian University of Technology, Liaoning Cancer Hospital & Institute, No.44 Xiaoheyan Road, Dadong District, Shenyang, 110042, China
| | - Tao Yu
- Department of Medical Imaging, Cancer Hospital of Dalian University of Technology, Liaoning Cancer Hospital & Institute, No.44 Xiaoheyan Road, Dadong District, Shenyang, 110042, China.
| | - Huijian Wu
- School of Bioengineering & Key Laboratory of Protein Modification and Disease, Dalian University of Technology, Dalian, 116024, Liaoning Province, China.
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Yaghjyan L, Wang Z, Warner ET, Rosner B, Heine J, Tamimi RM. Reproductive Factors Related to Childbearing and a Novel Automated Mammographic Measure, V. Cancer Epidemiol Biomarkers Prev 2024; 33:804-811. [PMID: 38497795 PMCID: PMC11147729 DOI: 10.1158/1055-9965.epi-23-1318] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Revised: 02/06/2024] [Accepted: 03/13/2024] [Indexed: 03/19/2024] Open
Abstract
BACKGROUND We investigated the associations between several reproductive factors related to childbearing and the variation (V) measure (a novel, objective, single summary measure of breast image intensity) by menopausal status. METHODS Our study included 3,814 cancer-free women within the Nurses' Health Study (NHS) and NHSII cohorts. The data on reproductive variables and covariates were obtained from biennial questionnaires closest to the mammogram date. V-measures were obtained from mammographic images using a previously developed algorithm capturing the standard deviation of pixel values. We used multivariate linear regression to examine the associations of parity, age at first birth, time between menarche and first birth, time since last pregnancy, and lifetime breastfeeding duration with V-measure, adjusting for breast cancer risk factors, including the percentage of mammographic density (PMD). We further examined whether these associations were statistically accounted for (mediated) by PMD. RESULTS Among premenopausal women, none of the reproductive factors were associated with V. Among postmenopausal women, inverse associations of parity and positive associations of age at first birth with V were mediated by PMD (percent mediated: nulliparity: 66.7%, P < 0.0001; parity: 50.5%, P < 0.01; age at first birth 76.1%, P < 0.001) and were no longer significant in PMD-adjusted models. Lifetime duration of breastfeeding was positively associated with V [>36 vs. 0 ≤1 months β = 0.29; 95% confidence interval (CI) 0.07; 0.52, Ptrend < 0.01], independent of PMD. CONCLUSIONS Parity, age at first birth, and breastfeeding were associated with postmenopausal V. IMPACT This study highlights associations of reproductive factors with mammographic image intensity.
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Affiliation(s)
- Lusine Yaghjyan
- Department of Epidemiology, University of Florida, College of Public Health and Health Professions and College of Medicine, Gainesville, Florida
| | - Zifan Wang
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
| | - Erica T Warner
- Harvard Medical School, Boston, Massachusetts
- Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts
| | - Bernard Rosner
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts
| | - John Heine
- Department of Cancer Epidemiology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, Florida
| | - Rulla M Tamimi
- Department of Population Health Sciences, Weill Cornell Medicine, New York, New York
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Barnard ME, DuPré NC, Heine JJ, Fowler EE, Murthy DJ, Nelleke RL, Chan A, Warner ET, Tamimi RM. Reproductive risk factors for breast cancer and association with novel breast density measurements among Hispanic, Black, and White women. Breast Cancer Res Treat 2024; 204:309-325. [PMID: 38095811 PMCID: PMC10948301 DOI: 10.1007/s10549-023-07174-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Accepted: 11/02/2023] [Indexed: 12/21/2023]
Abstract
PURPOSE There are differences in the distributions of breast cancer incidence and risk factors by race and ethnicity. Given the strong association between breast density and breast cancer, it is of interest describe racial and ethnic variation in the determinants of breast density. METHODS We characterized racial and ethnic variation in reproductive history and several measures of breast density for Hispanic (n = 286), non-Hispanic Black (n = 255), and non-Hispanic White (n = 1694) women imaged at a single hospital. We quantified associations between reproductive factors and percent volumetric density (PVD), dense volume (DV), non-dense volume (NDV), and a novel measure of pixel intensity variation (V) using multivariable-adjusted linear regression, and tested for statistical heterogeneity by race and ethnicity. RESULTS Reproductive factors most strongly associated with breast density were age at menarche, parity, and oral contraceptive use. Variation by race and ethnicity was most evident for the associations between reproductive factors and NDV (minimum p-heterogeneity:0.008) and V (minimum p-heterogeneity:0.004) and least evident for PVD (minimum p-heterogeneity:0.042) and DV (minimum p-heterogeneity:0.041). CONCLUSION Reproductive choices, particularly those related to childbearing and oral contraceptive use, may contribute to racial and ethnic variation in breast density.
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Affiliation(s)
- Mollie E Barnard
- Slone Epidemiology Center, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, 02118, USA.
- University of Utah Intermountain Healthcare Department of Population Health Sciences, Huntsman Cancer Institute, University of Utah, Salt Lake City, UT, USA.
| | - Natalie C DuPré
- Department of Epidemiology and Population Health, School of Public Health and Information Sciences, University of Louisville, Louisville, KY, USA
| | - John J Heine
- Division of Population Sciences, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA
| | - Erin E Fowler
- Division of Population Sciences, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA
| | - Divya J Murthy
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Rebecca L Nelleke
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Ariane Chan
- Volpara Health Technologies Ltd., Wellington, New Zealand
| | - Erica T Warner
- Clinical Translational Epidemiology Unit, Department of Medicine, Mongan Institute, Massachusetts General Hospital, Boston, MA, USA
| | - Rulla M Tamimi
- Department of Population Health Sciences, Weill Cornell Medical, New York, NY, USA
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Pereira A, Garmendia ML, Leiva V, Corvalán C, Michels KB, Shepherd J. Breast composition during and after puberty: the Chilean Growth and Obesity Cohort Study. Breast Cancer Res 2024; 26:45. [PMID: 38475816 DOI: 10.1186/s13058-024-01793-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Accepted: 02/20/2024] [Indexed: 03/14/2024] Open
Abstract
BACKGROUND Breast density (BD) is a strong risk factor for breast cancer. Little is known about how BD develops during puberty. Understanding BD trajectories during puberty and its determinants could be crucial for promoting preventive actions against breast cancer (BC) at early ages. The objective of this research is to characterize % fibroglandular volume (%FGV), absolute fibroglandular volume (AFGV), and breast volume (BV) at different breast Tanner stages until 4-year post menarche in a Latino cohort and to assess determinants of high %FGV and AFGV during puberty and in a fully mature breast. METHODS This is a longitudinal follow-up of 509 girls from low-middle socioeconomic status of the Southeast area of Santiago, recruited at a mean age of 3.5 years. The inclusion criteria were singleton birth born, birthweight between 2500 and 4500 g with no medical or mental disorder. A trained dietitian measured weight and height since 3.5 years old and sexual maturation from 8 years old (breast Tanner stages and age at menarche onset). Using standardized methods, BD was measured using dual-energy X-ray absorptiometry (DXA) in various developmental periods (breast Tanner stage B1 until 4 years after menarche onset). RESULTS In the 509 girls, we collected 1,442 breast DXA scans; the mean age at Tanner B4 was 11.3 years. %FGV increased across breast Tanner stages and peaked 250 days after menarche. AFGV and BV peaked 2 years after menarche onset. Girls in the highest quartiles of %FGV, AFGV, and BV at Tanner B4 and B5 before menarche onset had the highest values thereafter until 4 years after menarche onset. The most important determinants of %FGV and AFGV variability were BMI z-score (R2 = 44%) and time since menarche (R2 = 42%), respectively. CONCLUSION We characterize the breast development during puberty, a critical window of susceptibility. Although the onset of menarche is a key milestone for breast development, we observed that girls in the highest quartiles of %FGV and AFGV tracked in that group afterwards. Following these participants in adulthood would be of interest to understand the changes in breast composition during this period and its potential link with BC risk.
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Affiliation(s)
- Ana Pereira
- Institute of Nutrition and Food Technology, University of Chile, Santiago, Chile
| | | | - Valeria Leiva
- Institute of Nutrition and Food Technology, University of Chile, Santiago, Chile
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
| | - Camila Corvalán
- Institute of Nutrition and Food Technology, University of Chile, Santiago, Chile
| | - Karin B Michels
- Department of Epidemiology, Fielding School of Public Health, University of California, Los Angeles, USA
- Institute for Prevention and Cancer Epidemiology, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany
| | - John Shepherd
- Population Sciences in the Pacific Program (Cancer Epidemiology), University of Hawaii Cancer Center, Honolulu, HI, USA
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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. [PMID: 36930943 PMCID: PMC10239347 DOI: 10.1158/1940-6207.capr-22-0482] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Revised: 01/23/2023] [Accepted: 03/14/2023] [Indexed: 03/19/2023]
Abstract
Breastfeeding is inversely associated with breast cancer risk but the associations of breastfeeding with mammographic breast density (MBD) are not clear. We investigated the association between breastfeeding and volumetric measures of MBD [volumetric percent density (VPD), dense volume (DV), and non-dense volume (NDV)] and evaluated whether it differs by race, menopausal status, and body mass index (BMI). The study population was comprised of 964 women (67% non-Hispanic White, 29% non-Hispanic Black) who had screening mammography at Washington University School of Medicine, St. Louis, MO. VPD, DV and NDV were log10 transformed. We performed multivariable linear regression models adjusted for age, BMI, family history of breast cancer, race, and age at menarche among all participants and exclusively in parous women. Mean age was 50.7 years. VPD was 12% lower among women who breastfed 0-6 months, [10β = 0.88, 95% confidence interval (CI; 0.79-0.98)] compared with nulliparous women. Breastfeeding was not associated with VPD among women who breastfed >7 months. Breastfeeding was inversely associated with DV [parous never breastfed: 10β = 0.93; 95% CI (0.83-1.04), breastfed 0-6 months: 10β = 0.91, 95% CI (0.79-1.05), breastfed 7-12 months: 10β = 0.94; 95% CI (0.81-1.10), breastfed >12 months: 10β = 0.87, 95% CI (0.78-0.98), Ptrend = 0.03]. BMI modified the association between breastfeeding and VPD. Women who breastfed for 0-6 months and had a BMI < 25 kg/m2 had lower VPD compared with nulliparous women, but among women with a BMI ≥ 25 kg/m2 there was no association (Pinteraction = 0.04). In this diverse study population, the association of breastfeeding with VPD appears to be modified by BMI, but not by race or menopausal status. Future research exploring the associations of breastfeeding with other mammographic features are needed. PREVENTION RELEVANCE Breastfeeding for up to 6 months may be associated with lower VPD among women with a BMI < 25 kg/m2. The potential role of MBD in mediating the associations of breastfeeding with breast cancer risk in a select group of women deserves further evaluation. See related Spotlight, p. 309.
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Affiliation(s)
- Kayla R. Getz
- Division of Public Health Sciences, Department of Surgery, Washington University School of Medicine, St. Louis, Missouri, USA
| | - Babatunde Adedokun
- Center for Clinical Cancer Genetics and Global Health, Department of Medicine, University of Chicago, Chicago, Illinois, USA
| | - Shuai Xu
- Division of Public Health Sciences, Department of Surgery, Washington University School of Medicine, St. Louis, Missouri, USA
| | - Adetunji T. Toriola
- Division of Public Health Sciences, Department of Surgery, Washington University School of Medicine, St. Louis, Missouri, USA
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Yaghjyan L, Heng YJ, Baker GM, Rosner BA, Tamimi RM. Associations of alcohol consumption with breast tissue composition. Breast Cancer Res 2023; 25:33. [PMID: 36998083 PMCID: PMC10061845 DOI: 10.1186/s13058-023-01638-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Accepted: 03/13/2023] [Indexed: 04/01/2023] Open
Abstract
BACKGROUND We investigated the associations of alcohol with percentage of epithelium, stroma, fibroglandular tissue (epithelium + stroma), and fat in benign breast biopsy samples. METHODS We included 857 cancer-free women with biopsy-confirmed benign breast disease within the Nurses' Health Study (NHS) and NHSII cohorts. Percentage of each tissue was measured on whole slide images using a deep-learning algorithm and then log-transformed. Alcohol consumption (recent and cumulative average) was assessed with semi-quantitative food frequency questionnaires. Regression estimates were adjusted for known breast cancer risk factors. All tests were 2-sided. RESULTS Alcohol was inversely associated with % of stroma and fibroglandular tissue (recent ≥ 22 g/day vs. none: stroma: β = - 0.08, 95% Confidence Interval [CI] - 0.13; - 0.03; fibroglandular: β = - 0.08, 95% CI - 0.13; - 0.04; cumulative ≥ 22 g/day vs. none: stroma: β = - 0.08, 95% CI - 0.13; - 0.02; fibroglandular: β = - 0.09, 95% CI - 0.14; - 0.04) and positively associated with fat % (recent ≥ 22 g/day vs. none: β = 0.30, 95% CI 0.03; 0.57; cumulative ≥ 22 g/day vs. none: β = 0.32, 95% CI 0.04; 0.61). In stratified analysis, alcohol consumption was not associated with tissue measures in premenopausal women. In postmenopausal women, cumulative alcohol use was inversely associated with % of stroma and fibroglandular tissue and positively associated with fat % (≥ 22 g/day vs. none: stroma: β = - 0.16, 95% CI - 0.28; - 0.07; fibroglandular: β = - 0.18, 95% CI - 0.28; - 0.07; fat: β = 0.61, 95% CI 0.01; 1.22), with similar results for recent alcohol use. CONCLUSION Our findings suggest that alcohol consumption is associated with smaller % of stroma and fibroglandular tissue and a greater % of fat in postmenopausal women. Future studies are warranted to confirm our findings and to elucidate the underlying biological mechanisms.
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Affiliation(s)
- Lusine Yaghjyan
- Department of Epidemiology, College of Public Health and Health Professions and College of Medicine, University of Florida, 2004 Mowry Rd., Gainesville, FL, 32610, USA.
| | - Yujing J Heng
- Department of Pathology, Harvard Medical School, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Gabrielle M Baker
- Department of Pathology, Harvard Medical School, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Bernard A Rosner
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Rulla M Tamimi
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA
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8
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Pawar SD, Sharma KK, Sapate SG, Yadav GY, Alroobaea R, Alzahrani SM, Hedabou M. Multichannel DenseNet Architecture for Classification of Mammographic Breast Density for Breast Cancer Detection. Front Public Health 2022; 10:885212. [PMID: 35548086 PMCID: PMC9081505 DOI: 10.3389/fpubh.2022.885212] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2022] [Accepted: 03/14/2022] [Indexed: 11/13/2022] Open
Abstract
Percentage mammographic breast density (MBD) is one of the most notable biomarkers. It is assessed visually with the support of radiologists with the four qualitative Breast Imaging Reporting and Data System (BIRADS) categories. It is demanding for radiologists to differentiate between the two variably allocated BIRADS classes, namely, “BIRADS C and BIRADS D.” Recently, convolution neural networks have been found superior in classification tasks due to their ability to extract local features with shared weight architecture and space invariance characteristics. The proposed study intends to examine an artificial intelligence (AI)-based MBD classifier toward developing a latent computer-assisted tool for radiologists to distinguish the BIRADS class in modern clinical progress. This article proposes a multichannel DenseNet architecture for MBD classification. The proposed architecture consists of four-channel DenseNet transfer learning architecture to extract significant features from a single patient's two a mediolateral oblique (MLO) and two craniocaudal (CC) views of digital mammograms. The performance of the proposed classifier is evaluated using 200 cases consisting of 800 digital mammograms of the different BIRADS density classes with validated density ground truth. The classifier's performance is assessed with quantitative metrics such as precision, responsiveness, specificity, and the area under the curve (AUC). The concluding preliminary outcomes reveal that this intended multichannel model has delivered good performance with an accuracy of 96.67% during training and 90.06% during testing and an average AUC of 0.9625. Obtained results are also validated qualitatively with the help of a radiologist expert in the field of MBD. Proposed architecture achieved state-of-the-art results with a fewer number of images and with less computation power.
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Affiliation(s)
- Shivaji D. Pawar
- Department of Computer Science and Engineering, Lovely Professional University, Jalandhar, India
- SIES Graduate School of Technology, Navi Mumbai, India
| | - Kamal K. Sharma
- School of Electronics and Electrical Engineering, Lovely Professional University, Jalandhar, India
- *Correspondence: Kamal K. Sharma
| | - Suhas G. Sapate
- Department of Computer Science and Engineering, Annasaheb Dange College of Engineering and Technology, Sangli, India
| | | | - Roobaea Alroobaea
- Department Computer Science, College of Computers and Information Technology, Taif University, Taif, Saudi Arabia
| | - Sabah M. Alzahrani
- Department Computer Science, College of Computers and Information Technology, Taif University, Taif, Saudi Arabia
| | - Mustapha Hedabou
- School of Computer Science, Mohammed VI Polytechnic University, Ben Guerir, Morocco
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9
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Factors Influencing Mammographic Density in Asian Women: A Retrospective Cohort Study in the Northeast Region of Peninsular Malaysia. Diagnostics (Basel) 2022; 12:diagnostics12040860. [PMID: 35453907 PMCID: PMC9032698 DOI: 10.3390/diagnostics12040860] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2022] [Revised: 03/28/2022] [Accepted: 03/28/2022] [Indexed: 02/05/2023] Open
Abstract
Mammographic density is a significant risk factor for breast cancer. In this study, we identified the risk factors of mammographic density in Asian women and quantified the impact of breast density on the severity of breast cancer. We collected data from Hospital Universiti Sains Malaysia, a research- and university-based hospital located in Kelantan, Malaysia. Multivariable logistic regression was performed to analyse the data. Five significant factors were found to be associated with mammographic density: age (OR: 0.94; 95% CI: 0.92, 0.96), number of children (OR: 0.88; 95% CI: 0.81, 0.96), body mass index (OR: 0.88; 95% CI: 0.85, 0.92), menopause status (yes vs. no, OR: 0.59; 95% CI: 0.42, 0.82), and BI-RADS classification (2 vs. 1, OR: 1.87; 95% CI: 1.22, 2.84; 3 vs. 1, OR: 3.25; 95% CI: 1.86, 5.66; 4 vs. 1, OR: 3.75; 95% CI: 1.88, 7.46; 5 vs. 1, OR: 2.46; 95% CI: 1.21, 5.02; 6 vs. 1, OR: 2.50; 95% CI: 0.65, 9.56). Similarly, the average predicted probabilities were higher among BI-RADS 3 and 4 classified women. Understanding mammographic density and its influencing factors aids in accurately assessing and screening dense breast women.
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Abstract
Menopause nomenclature varies in the scholarly literature making synthesis and interpretation of research findings difficult. Therefore, the present study aimed to review and discuss critical developments in menopause nomenclature; determine the level of heterogeneity amongst menopause definitions and compare them with the Stages of Reproductive Aging Workshop criteria. Definitions/criteria used to characterise premenopausal and postmenopausal status were extracted from 210 studies and 128 of these studies were included in the final analyses. The main findings were that 39.84% of included studies were consistent with STRAW classification of premenopause, whereas 70.31% were consistent with STRAW classification of postmenopause. Surprisingly, major inconsistencies relating to premenopause definition were due to a total lack of reporting of any definitions/criteria for premenopause (39.84% of studies). In contrast, only 20.31% did not report definitions/criteria for postmenopause. The present findings indicate that there is a significant amount of heterogeneity associated with the definition of premenopause, compared with postmenopause. We propose three key suggestions/recommendations, which can be distilled from these findings. Firstly, premenopause should be transparently operationalised and reported. Secondly, as a minimum requirement, regular menstruation should be defined as the number of menstrual cycles in a period of at least 3 months. Finally, the utility of introducing normative age-ranges as supplementary criterion for defining stages of reproductive ageing should be considered. The use of consistent terminology in research will enhance our capacity to compare results from different studies and more effectively investigate issues related to women's health and ageing.
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Affiliation(s)
- Ananthan Ambikairajah
- Centre for Research on Ageing, Health and Wellbeing, Australian National University, Canberra, ACT, 2601, Australia.
- Discipline of Psychology, Faculty of Health, University of Canberra, Building 12, 11 Kirinari Street, Canberra, ACT, 2617, Australia.
| | - Erin Walsh
- Centre for Research on Ageing, Health and Wellbeing, Australian National University, Canberra, ACT, 2601, Australia
| | - Nicolas Cherbuin
- Centre for Research on Ageing, Health and Wellbeing, Australian National University, Canberra, ACT, 2601, Australia
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11
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Yu T, Ye DM. The epidemiologic factors associated with breast density: A review. JOURNAL OF RESEARCH IN MEDICAL SCIENCES 2022; 27:53. [PMID: 36092490 PMCID: PMC9450246 DOI: 10.4103/jrms.jrms_962_21] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 10/31/2021] [Revised: 01/14/2022] [Accepted: 01/26/2022] [Indexed: 11/04/2022]
Abstract
In recent years, some studies have evaluated the epidemiologic factors associated with breast density. However, the variant and inconsistent results exist. In addition, breast density has been proved to be a significant risk factor associated with breast cancer. Our review summarized the published studies and emphasized the crucial factors including epidemiological factors associated with breast density. In addition, we also discussed the potential reasons for the discrepant results with risk factors. To decrease the incidence and mortality rates for breast cancer, in clinical practice, breast density should be included for clinical risk models in addition to epidemiological factors, and physicians should get more concentrate on those women with risk factors and provide risk-based breast cancer screening regimens.
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12
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Shamsi U, Afzal S, Shamsi A, Azam I, Callen D. Factors associated with mammographic breast density among women in Karachi Pakistan. BMC Womens Health 2021; 21:438. [PMID: 34972514 PMCID: PMC8720218 DOI: 10.1186/s12905-021-01538-4] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Accepted: 11/10/2021] [Indexed: 12/31/2022] Open
Abstract
Background There are no studies done to evaluate the distribution of mammographic breast density and factors associated with it among Pakistani women. Methods Participants included 477 women, who had received either diagnostic or screening mammography at two hospitals in Karachi Pakistan. Mammographic breast density was assessed using the Breast Imaging Reporting and Data System. In person interviews were conducted using a detailed questionnaire, to assess risk factors of interest, and venous blood was collected to measure serum vitamin D level at the end of the interview. To determine the association of potential factors with mammographic breast density, multivariable polytomous logistic regression was used. Results High-density mammographic breast density (heterogeneously and dense categories) was high and found in 62.4% of women. There was a significant association of both heterogeneously dense and dense breasts with women of a younger age group < 45 years (OR 2.68, 95% CI 1.60–4.49) and (OR 4.83, 95% CI 2.54–9.16) respectively. Women with heterogeneously dense and dense breasts versus fatty and fibroglandular breasts had a higher history of benign breast disease (OR 1.90, 95% CI 1.14–3.17) and (OR 3.61, 95% CI 1.90–6.86) respectively. There was an inverse relationship between breast density and body mass index. Women with dense breasts and heterogeneously dense breasts had lower body mass index (OR 0.94 95% CI 0.90–0.99) and (OR 0.81, 95% CI 0.76–0.87) respectively. There was no association of mammographic breast density with serum vitamin D levels, diet, and breast cancer. Conclusions The findings of a positive association of higher mammographic density with younger age and benign breast disease and a negative association between body mass index and breast density are important findings that need to be considered in developing screening guidelines for the Pakistani population.
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Affiliation(s)
- Uzma Shamsi
- School of Medicine, University of Adelaide, Adelaide, Australia.
| | - Shaista Afzal
- Department of Radiology, Aga Khan University, Karachi, Pakistan
| | - Azra Shamsi
- Department of Gynecology and Obstetrics, Combined Military Hospital, Karachi, Pakistan
| | - Iqbal Azam
- Department of Community Health Sciences, Aga Khan University, Karachi, Pakistan
| | - David Callen
- School of Medicine, University of Adelaide, Adelaide, Australia
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Abstract
Globally, more than 2 million new cases of breast cancer are reported annually. The United States alone has more than 496,000 new cases every year. The worldwide prevalence is approximately 6.8 million cases. Although many risk factors for breast cancer are not modifiable, understanding the role of the factors that can be altered is critical. Alcohol consumption is a modifiable factor. Studies of alcohol in relation to breast cancer incidence have included hundreds of thousands of women. Evidence is consistent that intake, even intake of less than 10–15 grams per day, is associated with increased risk of this disease. In addition, evidence, although less extensive, shows that possible early indicators of risk, such as benign breast disease and increased breast density, are associated with alcohol consumption. Evidence is less strong for differences based on geographic region, beverage type, drinking pattern, or breast cancer subtype. Some studies have examined the association between alcohol and recurrence or survival after a breast cancer diagnosis. These findings are less consistent. Public awareness of alcohol as a risk factor for breast cancer is low, and public health measures to increase that awareness are warranted.
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Affiliation(s)
- Jo L Freudenheim
- School of Public Health and Health Professions, University at Buffalo, Buffalo, New York
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14
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Pubertal timing and breast density in young women: a prospective cohort study. Breast Cancer Res 2019; 21:122. [PMID: 31727127 PMCID: PMC6857297 DOI: 10.1186/s13058-019-1209-x] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2019] [Accepted: 10/15/2019] [Indexed: 01/11/2023] Open
Abstract
Background Earlier age at onset of pubertal events and longer intervals between them (tempo) have been associated with increased breast cancer risk. It is unknown whether the timing and tempo of puberty are associated with adult breast density, which could mediate the increased risk. Methods From 1988 to 1997, girls participating in the Dietary Intervention Study in Children (DISC) were clinically assessed annually between ages 8 and 17 years for Tanner stages of breast development (thelarche) and pubic hair (pubarche), and onset of menses (menarche) was self-reported. In 2006–2008, 182 participants then aged 25–29 years had their percent dense breast volume (%DBV) measured by magnetic resonance imaging. Multivariable, linear mixed-effects regression models adjusted for reproductive factors, demographics, and body size were used to evaluate associations of age and tempo of puberty events with %DBV. Results The mean (standard deviation) and range of %DBV were 27.6 (20.5) and 0.2–86.1. Age at thelarche was negatively associated with %DBV (p trend = 0.04), while pubertal tempo between thelarche and menarche was positively associated with %DBV (p trend = 0.007). %DBV was 40% higher in women whose thelarche-to-menarche tempo was 2.9 years or longer (geometric mean (95%CI) = 21.8% (18.2–26.2%)) compared to women whose thelarche-to-menarche tempo was less than 1.6 years (geometric mean (95%CI) = 15.6% (13.9–17.5%)). Conclusions Our results suggest that a slower pubertal tempo, i.e., greater number of months between thelarche and menarche, is associated with higher percent breast density in young women. Future research should examine whether breast density mediates the association between slower tempo and increased breast cancer risk.
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Ambikairajah A, Walsh E, Tabatabaei-Jafari H, Cherbuin N. Fat mass changes during menopause: a metaanalysis. Am J Obstet Gynecol 2019; 221:393-409.e50. [PMID: 31034807 DOI: 10.1016/j.ajog.2019.04.023] [Citation(s) in RCA: 112] [Impact Index Per Article: 22.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2018] [Revised: 03/15/2019] [Accepted: 04/19/2019] [Indexed: 02/08/2023]
Abstract
OBJECTIVE Data: Fat mass has been shown to increase in aging women; however, the extent to which menopausal status mediates these changes remains unclear. The purpose of this review was to determine (1) how fat mass differs in quantity and distribution between premenopausal and postmenopausal women, (2) whether and how age and/or menopausal status moderates any observed differences, and (3) which type of fat mass measure is best suited to the detection of differences in fat mass between groups. STUDY This review with metaanalyses is reported according to Metaanalysis of Observational Studies in Epidemiology guidelines. STUDY APPRAISAL AND SYNTHESIS METHODS Studies (published up to May 2018) were identified via PubMed to provide fat mass measures in premenopausal and postmenopausal women. We included 201 cross-sectional studies in the metaanalysis, which provided a combined sample size of 1,049,919 individuals and consisted of 478,734 premenopausal women and 571,185 postmenopausal women. Eleven longitudinal studies were included in the metaanalyses, which provided a combined sample size of 2472 women who were premenopausal at baseline and postmenopausal at follow up. RESULTS The main findings of this review were that fat mass significantly increased between premenopausal and postmenopausal women across most measures, which included body mass index (1.14 kg/m2; 95% confidence interval, 0.95-1.32 kg/m2), bodyweight (1 kg; 95% confidence interval, 0.44-1.57 kg), body fat percentage (2.88%; 95% confidence interval, 2.13-3.63%), waist circumference (4.63 cm; 95% confidence interval, 3.90-5.35 cm), hip circumference (2.01 cm; 95% confidence interval, 1.36-2.65 cm), waist-hip ratio (0.04; 95% confidence interval, 0.03-0.05), visceral fat (26.90 cm2; 95% confidence interval, 13.12-40.68), and trunk fat percentage (5.49%; 95% confidence interval, 3.91-7.06 cm2). The exception was total leg fat percentage, which significantly decreased (-3.19%; 95% confidence interval, -5.98 to -0.41%). No interactive effects were observed between menopausal status and age across all fat mass measures. CONCLUSION The change in fat mass quantity between premenopausal and postmenopausal women was attributable predominantly to increasing age; menopause had no significant additional influence. However, the decrease in total leg fat percentage and increase in measures of central fat are indicative of a possible change in fat mass distribution after menopause. These changes are likely to, at least in part, be due to hormonal shifts that occur during midlife when women have a higher androgen (ie, testosterone) to estradiol ratio after menopause, which has been linked to enhanced central adiposity deposition. Evidently, these findings suggest attention should be paid to the accumulation of central fat after menopause, whereas increases in total fat mass should be monitored consistently across the lifespan.
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Rey-Vargas L, Sanabria-Salas MC, Fejerman L, Serrano-Gómez SJ. Risk Factors for Triple-Negative Breast Cancer among Latina Women. Cancer Epidemiol Biomarkers Prev 2019; 28:1771-1783. [DOI: 10.1158/1055-9965.epi-19-0035] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2019] [Revised: 03/15/2019] [Accepted: 08/19/2019] [Indexed: 11/16/2022] Open
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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. [PMID: 30639525 DOI: 10.1016/j.clinimag.2019.01.002] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2018] [Revised: 12/10/2018] [Accepted: 01/04/2019] [Indexed: 12/20/2022]
Abstract
PURPOSE Improved mammographic sensitivity is associated with reduced mammographic density. This study aims to: provide a preliminary report on mammographic density among women in Riyadh; identify risk factors associated with mammographic density; and consider the potential implications for screening practices. METHODS Based on a cross-sectional design, we examined a total of 792 women using an area-based mammographic density method (LIBRA). Spearman's correlation, Mann-Whitney U, Kruskal-Wallis and binary logistic regression were used for analyses. RESULTS The study population had a mean age of 49.6 years and a high proportion of participants were overweight or obese (90%). A large number of women had low mammographic density, with a mean dense breast area of 19.1 cm2 and percent density of 10.3 cm2. Slightly more than half of the variations in the dense breast area and percent density models were explained by BMI. In the adjusted analyses, BMI, menopausal status, age at menarche and number of children remained statistically significant predictors. CONCLUSION Given the high proportion of women with low mammographic density, our findings suggest that women living in Riyadh may not require additional imaging strategies beyond mammography to detect breast cancers. The high proportion of obese women reported here and across Saudi Arabia suggests that mammographic density is less likely to have an adverse impact on mammographic sensitivity. Thus and to improve clinical outcomes among Saudi women, annual mammography and commencing screening at a younger age are suggested. Additional studies are required to shed further light on our findings.
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Affiliation(s)
- Salman M Albeshan
- Medical Radiation Sciences, Medical Image Optimization and Perception Group (MIOPeG), Australia; Department of Radiological Sciences, College of Applied Medical Sciences, King Saud University (KSU), Saudi Arabia.
| | - Syeda Z Hossain
- Discipline of Behavioral and Social Sciences in Health, Australia
| | | | - Jennifer K Peat
- Medical Radiation Sciences, Medical Image Optimization and Perception Group (MIOPeG), Australia
| | | | - Patrick C Brennan
- Medical Radiation Sciences, Medical Image Optimization and Perception Group (MIOPeG), Australia
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Ciritsis A, Rossi C, Vittoria De Martini I, Eberhard M, Marcon M, Becker AS, Berger N, Boss A. Determination of mammographic breast density using a deep convolutional neural network. Br J Radiol 2018; 92:20180691. [PMID: 30209957 DOI: 10.1259/bjr.20180691] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023] Open
Abstract
OBJECTIVE High breast density is a risk factor for breast cancer. The aim of this study was to develop a deep convolutional neural network (dCNN) for the automatic classification of breast density based on the mammographic appearance of the tissue according to the American College of Radiology Breast Imaging Reporting and Data System (ACR BI-RADS) Atlas. METHODS In this study, 20,578 mammography single views from 5221 different patients (58.3 ± 11.5 years) were downloaded from the picture archiving and communications system of our institution and automatically sorted according to the ACR density (a-d) provided by the corresponding radiological reports. A dCNN with 11 convolutional layers and 3 fully connected layers was trained and validated on an augmented dataset. The model was finally tested on two different datasets against: i) the radiological reports and ii) the consensus decision of two human readers. None of the test datasets was part of the dataset used for the training and validation of the algorithm. RESULTS The optimal number of epochs was 91 for medio-lateral oblique (MLO) projections and 94 for cranio-caudal projections (CC), respectively. Accuracy for MLO projections obtained on the validation dataset was 90.9% (CC: 90.1%). Tested on the first test dataset of mammographies (850 MLO and 880 CC), the algorithm showed an accordance with the corresponding radiological reports of 71.7% for MLO and of 71.0% for CC. The agreement with the radiological reports improved in the differentiation between dense and fatty breast for both projections (MLO = 88.6% and CC = 89.9%). In the second test dataset of 200 mammographies, a good accordance was found between the consensus decision of the two readers on both, the MLO-model (92.2%) and the right craniocaudal-model (87.4%). In the differentiation between fatty (ACR A/B) and dense breasts (ACR C/D), the agreement reached 99% for the MLO and 96% for the CC projections, respectively. CONCLUSIONS The dCNN allows for accurate classification of breast density based on the ACR BI-RADS system. The proposed technique may allow accurate, standardized, and observer independent breast density evaluation of mammographies. ADVANCES IN KNOWLEDGE Standardized classification of mammographies by a dCNN could lead to a reduction of falsely classified breast densities, thereby allowing for a more accurate breast cancer risk assessment for the individual patient and a more reliable decision, whether additional ultrasound is recommended.
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Affiliation(s)
- Alexander Ciritsis
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, Zürich, Switzerland
| | - Cristina Rossi
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, Zürich, Switzerland
| | | | - Matthias Eberhard
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, Zürich, Switzerland
| | - Magda Marcon
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, Zürich, Switzerland
| | - Anton S Becker
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, Zürich, Switzerland
| | - Nicole Berger
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, Zürich, Switzerland
| | - Andreas Boss
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, Zürich, Switzerland
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Binder AM, Corvalan C, Mericq V, Pereira A, Santos JL, Horvath S, Shepherd J, Michels KB. Faster ticking rate of the epigenetic clock is associated with faster pubertal development in girls. Epigenetics 2018; 13:85-94. [PMID: 29235933 DOI: 10.1080/15592294.2017.1414127] [Citation(s) in RCA: 75] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022] Open
Abstract
Epigenetic age is an indicator of biological aging, capturing the impact of environmental and behavioral influences across time on cellular function. Deviance between epigenetic age and chronological age (AgeAccel) is a predictor of health. Pubertal timing has similarly been associated with cancer risk and mortality rate among females. We examined the association between AgeAccel and pubertal timing and adolescent breast composition in the longitudinal Growth and Obesity Cohort Study. AgeAccel was estimated in whole blood using the Horvath method at breast Tanner 2 (B2) and 4 (B4). Total breast volume, absolute fibro-glandular volume (FGV), and %FGV were evaluated at B4 using dual X-ray absorptiometry. The impact of AgeAccel (mean: 0; SD: 3.78) across puberty on the time to breast development (thelarche), menarche, and pubertal tempo (thelarche to menarche) was estimated using accelerated failure time models; generalized estimating equations were used to evaluate associations with breast density. A five-year increase in average adolescent AgeAccel was associated with a significant decrease in time to menarche [hazard ratio (HR): 1.37; 95% confidence interval (CI): 1.04, 1.80] adjusting for birth weight, maternal pre-pregnancy body mass index, maternal height, maternal education, B2 height, fat percentage, and cell composition. AgeAccel displayed a stronger inverse association with pubertal tempo (HR: 1.48; 95% CI: 1.10, 1.99). A five-year increase in AgeAccel was associated with 5% greater %FGV, adjusting for B4 percent body fat, and maternal traits (95% CI: 1.01, 1.10). Our study provides unique insight into the influence of AgeAccel on pubertal development in girls, which may have implications for adult health.
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Affiliation(s)
- Alexandra M Binder
- a Department of Epidemiology , Fielding School of Public Health, University of California , Los Angeles , 650 Charles E Young Drive South, Los Angeles , CA 90095 , USA
| | - Camila Corvalan
- b Institute of Nutrition and Food Technology , University of Chile , Av el Libano 5524, Santiago , Chile
| | - Verónica Mericq
- c Institute of Maternal and Child Research , University of Chile , Santa Rosa 1234, 2° piso, Santiago , Chile
| | - Ana Pereira
- b Institute of Nutrition and Food Technology , University of Chile , Av el Libano 5524, Santiago , Chile
| | - José Luis Santos
- d Department of Nutrition , Diabetes and Metabolism, School of Medicine, Pontificia Universidad Católica de Chile , Av Libertador Bernardo O'Higgins 340, Santiago , Chile
| | - Steve Horvath
- e Department of Biostatistics , School of Public Health, and Department of Human Genetics, Gonda Research Center , David Geffen School of Medicine, University of California, Los Angeles , 695 Charles E Young Drive South, Los Angeles , CA 90095 , USA
| | - John Shepherd
- f Department of Radiology and Biomedical Imaging , University of California, San Francisco , 400 Parnassus Avenue, San Francisco , CA 94117 , USA
| | - Karin B Michels
- a Department of Epidemiology , Fielding School of Public Health, University of California , Los Angeles , 650 Charles E Young Drive South, Los Angeles , CA 90095 , USA
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Liu Y, Tamimi RM, Colditz GA, Bertrand KA. Alcohol consumption across the life course and mammographic density in premenopausal women. Breast Cancer Res Treat 2018; 167:529-535. [PMID: 28952004 PMCID: PMC5792299 DOI: 10.1007/s10549-017-4517-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2017] [Accepted: 09/18/2017] [Indexed: 10/18/2022]
Abstract
PURPOSE Consumption of alcoholic beverages during adolescence and early adulthood has been consistently associated with higher breast cancer risk. The influence of alcohol consumption early in life on mammographic breast density, a marker of breast cancer risk, is inconclusive. This study examined associations of alcohol consumption across the life course with premenopausal mammographic density. METHODS The study population included 1211 premenopausal women in the Nurses' Health Study II without cancer, who recalled their alcohol consumption at age 15 through enrollment in 1989 (baseline), and had mammograms available. Recent alcohol consumption was updated over follow-up. Percent and absolute measures of mammographic density were quantified on digitized film mammograms. Generalized linear regression was used to assess associations. RESULTS There were no notable differences in any of the three density measures for alcohol consumption at any age (15-17, 18-22, 23-30, and 31-mammogram). Neither alcohol consumption before first pregnancy nor after first pregnancy was significantly associated with any of the three density measures. CONCLUSIONS Moderate alcohol consumption during different age intervals during adolescence and early adulthood was not associated with mammographic density in premenopausal women.
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Affiliation(s)
- Ying Liu
- Division of Public Health Sciences, Department of Surgery, Washington University School of Medicine, St. Louis, MO, USA
- Alvin J. Siteman Cancer Center at Barnes-Jewish Hospital and Washington University School of Medicine, St. Louis, MO, USA
| | - Rulla M Tamimi
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Graham A Colditz
- Division of Public Health Sciences, Department of Surgery, Washington University School of Medicine, St. Louis, MO, USA
- Alvin J. Siteman Cancer Center at Barnes-Jewish Hospital and Washington University School of Medicine, St. Louis, MO, USA
| | - Kimberly A Bertrand
- Slone Epidemiology Center at Boston University, 72 East Concord Street, L-7, Boston, MA, 02118, USA.
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21
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Burton A, Maskarinec G, Perez-Gomez B, Vachon C, Miao H, Lajous M, López-Ridaura R, Rice M, Pereira A, Garmendia ML, Tamimi RM, Bertrand K, Kwong A, Ursin G, Lee E, Qureshi SA, Ma H, Vinnicombe S, Moss S, Allen S, Ndumia R, Vinayak S, Teo SH, Mariapun S, Fadzli F, Peplonska B, Bukowska A, Nagata C, Stone J, Hopper J, Giles G, Ozmen V, Aribal ME, Schüz J, Van Gils CH, Wanders JOP, Sirous R, Sirous M, Hipwell J, Kim J, Lee JW, Dickens C, Hartman M, Chia KS, Scott C, Chiarelli AM, Linton L, Pollan M, Flugelman AA, Salem D, Kamal R, Boyd N, dos-Santos-Silva I, McCormack V. Mammographic density and ageing: A collaborative pooled analysis of cross-sectional data from 22 countries worldwide. PLoS Med 2017; 14:e1002335. [PMID: 28666001 PMCID: PMC5493289 DOI: 10.1371/journal.pmed.1002335] [Citation(s) in RCA: 103] [Impact Index Per Article: 14.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/06/2017] [Accepted: 05/24/2017] [Indexed: 12/18/2022] Open
Abstract
BACKGROUND Mammographic density (MD) is one of the strongest breast cancer risk factors. Its age-related characteristics have been studied in women in western countries, but whether these associations apply to women worldwide is not known. METHODS AND FINDINGS We examined cross-sectional differences in MD by age and menopausal status in over 11,000 breast-cancer-free women aged 35-85 years, from 40 ethnicity- and location-specific population groups across 22 countries in the International Consortium on Mammographic Density (ICMD). MD was read centrally using a quantitative method (Cumulus) and its square-root metrics were analysed using meta-analysis of group-level estimates and linear regression models of pooled data, adjusted for body mass index, reproductive factors, mammogram view, image type, and reader. In all, 4,534 women were premenopausal, and 6,481 postmenopausal, at the time of mammography. A large age-adjusted difference in percent MD (PD) between post- and premenopausal women was apparent (-0.46 cm [95% CI: -0.53, -0.39]) and appeared greater in women with lower breast cancer risk profiles; variation across population groups due to heterogeneity (I2) was 16.5%. Among premenopausal women, the √PD difference per 10-year increase in age was -0.24 cm (95% CI: -0.34, -0.14; I2 = 30%), reflecting a compositional change (lower dense area and higher non-dense area, with no difference in breast area). In postmenopausal women, the corresponding difference in √PD (-0.38 cm [95% CI: -0.44, -0.33]; I2 = 30%) was additionally driven by increasing breast area. The study is limited by different mammography systems and its cross-sectional rather than longitudinal nature. CONCLUSIONS Declines in MD with increasing age are present premenopausally, continue postmenopausally, and are most pronounced over the menopausal transition. These effects were highly consistent across diverse groups of women worldwide, suggesting that they result from an intrinsic biological, likely hormonal, mechanism common to women. If cumulative breast density is a key determinant of breast cancer risk, younger ages may be the more critical periods for lifestyle modifications aimed at breast density and breast cancer risk reduction.
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Affiliation(s)
- Anya Burton
- Section of Environment and Radiation, International Agency for Research on Cancer, Lyon, France
| | - Gertraud Maskarinec
- University of Hawaii Cancer Center, Honolulu, Hawaii, United States of America
| | | | - Celine Vachon
- Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, United States of America
| | - Hui Miao
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore
| | - Martín Lajous
- Instituto Nacional de Salud Pública, Cuernavaca, Mexico
| | | | - Megan Rice
- Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Ana Pereira
- Instituto de Nutrición y Tecnología de los Alimentos, Universidad de Chile, Santiago, Chile
| | - Maria Luisa Garmendia
- Instituto de Nutrición y Tecnología de los Alimentos, Universidad de Chile, Santiago, Chile
| | - Rulla M. Tamimi
- Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Kimberly Bertrand
- Slone Epidemiology Center, Boston University, Boston, Massachusetts, United States of America
| | - Ava Kwong
- Division of Breast Surgery, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong, China
- Department of Surgery and Cancer Genetics Center, Hong Kong Sanatorium and Hospital, Hong Kong, China
- Hong Kong Hereditary Breast Cancer Family Registry, Hong Kong, China
| | - Giske Ursin
- Cancer Registry of Norway, Oslo, Norway
- Department of Nutrition, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, United States of America
| | - Eunjung Lee
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, United States of America
| | - Samera A. Qureshi
- Norwegian Centre for Migrant and Minority Health (NAKMI), Oslo, Norway
| | - Huiyan Ma
- Department of Population Sciences, City of Hope National Medical Center, Duarte, California, United States of America
| | - Sarah Vinnicombe
- Division of Cancer Research, Ninewells Hospital and Medical School, Dundee, United Kingdom
| | - Sue Moss
- Wolfson Institute of Preventive Medicine, Queen Mary University of London, London, United Kingdom
| | - Steve Allen
- Department of Diagnostic Radiology, Royal Marsden NHS Foundation Trust, London, United Kingdom
| | - Rose Ndumia
- Aga Khan University Hospital, Nairobi, Kenya
| | | | - Soo-Hwang Teo
- Breast Cancer Research Group, University of Malaya Medical Centre, University of Malaya, Kuala Lumpur, Malaysia
- Cancer Research Malaysia, Subang Jaya, Malaysia
| | | | - Farhana Fadzli
- Breast Cancer Research Unit, Faculty of Medicine, University of Malaya Cancer Research Institute, University of Malaya, Kuala Lumpur, Malaysia
- Biomedical Imaging Department, University of Malaya Medical Centre, University of Malaya, Kuala Lumpur, Malaysia
| | | | | | - Chisato Nagata
- Department of Epidemiology & Preventive Medicine, Graduate School of Medicine, Gifu University, Gifu, Japan
| | - Jennifer Stone
- Centre for Genetic Origins of Health and Disease, University of Western Australia, Crawley, Western Australia, Australia
| | - John Hopper
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, Victoria, Australia
| | - Graham Giles
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, Victoria, Australia
- Cancer Epidemiology Centre, Cancer Council Victoria, Melbourne, Victoria, Australia
| | - Vahit Ozmen
- Faculty of Medicine, Istanbul University, Istanbul, Turkey
| | - Mustafa Erkin Aribal
- Department of Radiology, School of Medicine, Marmara University, Istanbul, Turkey
| | - Joachim Schüz
- Section of Environment and Radiation, International Agency for Research on Cancer, Lyon, France
| | - Carla H. Van Gils
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Johanna O. P. Wanders
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Reza Sirous
- Isfahan University of Medical Sciences, Isfahan, Iran
| | - Mehri Sirous
- Radiology Department, Isfahan University of Medical Sciences, Isfahan, Iran
| | - John Hipwell
- Centre for Medical Image Computing, University College London, London, United Kingdom
| | - Jisun Kim
- Asan Medical Center, Seoul, Republic of Korea
| | | | - Caroline Dickens
- Department of Internal Medicine, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Mikael Hartman
- Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, United States of America
- Department of Surgery, Yong Loo Lin School of Medicine, Singapore
| | - Kee-Seng Chia
- Graduate School for Integrative Sciences and Engineering, National University of Singapore, Singapore
| | - Christopher Scott
- Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, United States of America
| | - Anna M. Chiarelli
- Ontario Breast Screening Program, Cancer Care Ontario, Toronto, Ontario, Canada
| | - Linda Linton
- Princess Margaret Cancer Centre, Toronto, Ontario, Canada
| | - Marina Pollan
- Instituto de Salud Carlos III, Madrid, Spain
- CIBERESP, Madrid, Spain
| | - Anath Arzee Flugelman
- National Cancer Control Center, Lady Davis Carmel Medical Center, Faculty of Medicine, Technion–Israel Institute of Technology, Haifa, Israel
| | - Dorria Salem
- Woman Imaging Unit, Radiodiagnosis Department, Kasr El Aini, Cairo University Hospitals, Cairo, Egypt
| | - Rasha Kamal
- Woman Imaging Unit, Radiodiagnosis Department, Kasr El Aini, Cairo University Hospitals, Cairo, Egypt
| | - Norman Boyd
- Princess Margaret Cancer Centre, Toronto, Ontario, Canada
| | - Isabel dos-Santos-Silva
- Department of Non-communicable Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, United Kingdom
| | - Valerie McCormack
- Section of Environment and Radiation, International Agency for Research on Cancer, Lyon, France
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22
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Rice MS, Rosner BA, Tamimi RM. Percent mammographic density prediction: development of a model in the nurses' health studies. Cancer Causes Control 2017; 28:677-684. [PMID: 28478536 DOI: 10.1007/s10552-017-0898-7] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2016] [Accepted: 04/22/2017] [Indexed: 11/28/2022]
Abstract
PURPOSE To develop a model to predict percent mammographic density (MD) using questionnaire data and mammograms from controls in the Nurses' Health Studies' nested breast cancer case-control studies. Further, we assessed the association between both measured and predicted percent MD and breast cancer risk. METHODS Using data from 2,955 controls, we assessed several variables as potential predictors. We randomly divided our dataset into a training dataset (two-thirds of the dataset) and a testing dataset (one-third of the dataset). We used stepwise linear regression to identify the subset of variables that were most predictive. Next, we examined the correlation between measured and predicted percent MD in the testing dataset and computed the r 2 in the total dataset. We used logistic regression to examine the association between measured and predicted percent MD and breast cancer risk. RESULTS In the training dataset, several variables were selected for inclusion, including age, body mass index, and parity, among others. In the testing dataset, the Spearman correlation coefficient between predicted and measured percent MD was 0.61. As the prediction model performed well in the testing dataset, we developed the final model in the total dataset. The final prediction model explained 41% of the variability in percent MD. Both measured and predicted percent MD were similarly associated with breast cancer risk adjusting for age, menopausal status, and hormone use (OR per five unit increase = 1.09 for both). CONCLUSION These results suggest that predicted percent MD may be useful for research studies in which mammograms are unavailable.
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Affiliation(s)
- Megan S Rice
- Clinical and Translational Epidemiology Unit, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, 55 Fruit Street, Bartlett 9, Boston, MA, 02114, USA.
| | - Bernard A Rosner
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, 02114, USA
| | - Rulla M Tamimi
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, 02114, USA.,Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
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Ziembicki S, Zhu J, Tse E, Martin LJ, Minkin S, Boyd NF. The Association between Alcohol Consumption and Breast Density: A Systematic Review and Meta-analysis. Cancer Epidemiol Biomarkers Prev 2016; 26:170-178. [DOI: 10.1158/1055-9965.epi-16-0522] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2016] [Revised: 09/01/2016] [Accepted: 09/14/2016] [Indexed: 11/16/2022] Open
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24
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Beltran-Sarmiento E, Floriano-Sánchez E, Bandala C, Lara-Padilla E, Cárdenas-Rodríguez N. Association of CYP8A1 (Prostacyclin I2 synthase) polymorphism rs5602 with breast cancer in Mexican woman. Am J Cancer Res 2016; 6:341-349. [PMID: 27186408 PMCID: PMC4859665] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2015] [Accepted: 12/07/2015] [Indexed: 06/05/2023] Open
Abstract
Breast cancer (BCa) is the most common cancer in Mexican women. Certain risk factors, such as environmental and lifestyle factors have been implicated in BCa initiation and progression. Moreover, genetic factors, such as single nucleotide polymorphisms (SNPs) of the P450 system, have been reported in BCa. In this report, and for the first time in the literature, we analyzed the rs5602 (67730 T > C) polymorphism in the CYP8A1 in patients with BCa and in healthy Mexican women to identify a potential risk between this polymorphism and BCa. Leukocyte cells from 38 control patients and tissue from radical mastectomy surgeries in 64 BCa patients were used for polymorphism analysis using an allelic discrimination assay with TaqMan probes. Links with clinic-pathological characteristics were also analyzed. Statistical analysis was performed using the standard χ(2) or Fisher exact test statistic. All CYP8A1 genotypes were detected in patients with BCa and the controls. Significant differences were observed in the distribution of CYP8A1 genotypes between the patients and controls (P=0.0008) and allele C was significantly associated with BCa risk (OR 2.08, 95% CI 1.166-3.72, P=0.0178). All polymorphism frequencies were in Hardy-Weinberg Equilibrium (HWE) in the controls (P > 0.05). We found that variant 67730 T > C was significantly associated with an increased risk of BCa (P < 0.05). We not observed an association of the TT and TC + CC genotypes with the clinical stage, BIRADS, estrogen receptor (ER) status, progesterone receptor (PR) status, HER2 status, p53 status, CD34 status, metastasis or therapy use. These results indicate that the CYP8A1 rs5602 SNP is a possible risk factor for BCa in Mexican women. This study showed an association between the CYP8A1 polymorphism and BCa risk in a Mexican population.
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Affiliation(s)
| | - Esaú Floriano-Sánchez
- Multidisciplinary Research Laboratory, Military School of Graduate of Health, SEDENA México 11200, D.F., México
| | - Cindy Bandala
- Department of Neuroscience, National Rehabilitation Institute México 14389, D.F., México
| | - Eleazar Lara-Padilla
- Section of Research and Graduate Studies, National Polytechnic Institute México 11340, D.F., México
| | - Noemí Cárdenas-Rodríguez
- Laboratory of Neurosciences (Neurochemistry), National Institute of Pediatrics México 04530, D.F., México
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