Disentangling the relationships of body mass index and circulating sex hormone concentrations in mammographic density using Mendelian randomization

Cameron B Haas; Hongjie Chen; Tabitha Harrison; Shaoqi Fan; Manuela Gago-Dominguez; Jose E Castelao; Manjeet K Bolla; Qin Wang; Joe Dennis; Kyriaki Michailidou; Alison M Dunning; Douglas F Easton; Antonis C Antoniou; Per Hall; Kamila Czene; Irene L Andrulis; Anna Marie Mulligan; Roger L Milne; Peter A Fasching; Lothar Haeberle; Montserrat Garcia-Closas; Thomas Ahearn; Gretchen L Gierach; Christopher Haiman; Gertraud Maskarinec; Fergus J Couch; Janet E Olson; Esther M John; Geogia Chenevix-Trench; Amy Berrington de Gonzalez; Michael Jones; Jennifer Stone; Rachel Murphy; Kristan J Aronson; Karen J Wernli; Li Hsu; Celine Vachon; Rulla M Tamimi; Sara Lindström

doi:10.1007/s10549-024-07306-w

Disentangling the relationships of body mass index and circulating sex hormone concentrations in mammographic density using Mendelian randomization

Breast Cancer Res Treat. 2024 Apr 24. doi: 10.1007/s10549-024-07306-w. Online ahead of print.

Authors

Cameron B Haas^{1

2}, Hongjie Chen³, Tabitha Harrison³, Shaoqi Fan⁴, Manuela Gago-Dominguez⁵, Jose E Castelao⁶, Manjeet K Bolla⁷, Qin Wang⁷, Joe Dennis⁷, Kyriaki Michailidou⁸, Alison M Dunning⁹, Douglas F Easton^{7

9}, Antonis C Antoniou⁷, Per Hall^{10

11}, Kamila Czene¹⁰, Irene L Andrulis^{12

13}, Anna Marie Mulligan^{14

15}, Roger L Milne^{16

17

18}, Peter A Fasching¹⁹, Lothar Haeberle¹⁹, Montserrat Garcia-Closas^{4

20}, Thomas Ahearn⁴, Gretchen L Gierach⁴, Christopher Haiman²¹, Gertraud Maskarinec²², Fergus J Couch²³, Janet E Olson²⁴, Esther M John^{25

26}, Geogia Chenevix-Trench²⁷, Amy Berrington de Gonzalez²⁰, Michael Jones²⁰, Jennifer Stone^{28

29}, Rachel Murphy^{30

31}, Kristan J Aronson³², Karen J Wernli³³, Li Hsu^{25

34}, Celine Vachon³⁵, Rulla M Tamimi^{36

37}, Sara Lindström^{3

25}

Affiliations

¹ Department of Epidemiology, University of Washington, Seattle, WA, USA. Cameron.b.haas@gmail.com.
² Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA, USA. Cameron.b.haas@gmail.com.
³ Department of Epidemiology, University of Washington, Seattle, WA, USA.
⁴ Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA.
⁵ Health Research Institute of Santiago de Compostela Foundation (FIDIS), SERGAS, Cancer Genetics and Epidemiology Group, Santiago, Spain.
⁶ Unidad de Oncología Genética, Instituto de Investigación Sanitaria, Galicia Sur, Vigo, Spain.
⁷ Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
⁸ Biostatistics Unit, The Cyprus Institute of Neurology & Genetics, Nicosia, Cyprus.
⁹ Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, UK.
¹⁰ Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
¹¹ Department of Oncology, Södersjukhuset, Stockholm, Sweden.
¹² Fred A. Litwin Center for Cancer Genetics, Lunenfeld-Tanenbaum Research Institute of Mount Sinai Hospital, Toronto, ON, Canada.
¹³ Department of Molecular Genetics, University of Toronto, Toronto, Canada.
¹⁴ Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Canada.
¹⁵ Laboratory Medicine Program, University Health Network, Toronto, Canada.
¹⁶ Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Australia.
¹⁷ Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, Australia.
¹⁸ Prevision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC, Australia.
¹⁹ Department of Gynecology and Obstetrics, Comprehensive Cancer Center Erlangen-EMN, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nuremberg, Erlangen, Germany.
²⁰ Division of Genetics and Epidemiology, Institute of Cancer Research, London, UK.
²¹ Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
²² Population Sciences in the Pacific Program, University of Hawai'i Cancer Center, Honolulu, HI, USA.
²³ Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA.
²⁴ Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA.
²⁵ Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA, USA.
²⁶ Division of Oncology, Department of Medicine, Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA.
²⁷ Cancer Research Program, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia.
²⁸ Genetic Epidemiology Group, School of Population and Global Health, University of Western Australia, Perth, WA, Australia.
²⁹ Division of Cancer Medicine, Peter MacCallum Cancer Centre, The University of Melbourne, Melbourne, VIC, Australia.
³⁰ School of Population and Public Health, University of British Columbia, Vancouver, BC, Canada.
³¹ Cancer Control Research, BC Cancer Agency, Vancouver, BC, Canada.
³² Division of Cancer Care and Epidemiology, Department of Community Health and Epidemiology, Queen's University, Kingston, ON, K7L3N6, Canada.
³³ Kaiser Permanente Washington Health Research Institute, Seattle, WA, USA.
³⁴ Department of Biostatistics, University of Washington, Seattle, WA, USA.
³⁵ Division of Clinical Trials and Biostatistics, Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA.
³⁶ Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA.
³⁷ Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.

PMID: 38653906
DOI: 10.1007/s10549-024-07306-w

Abstract

Purpose: Mammographic density phenotypes, adjusted for age and body mass index (BMI), are strong predictors of breast cancer risk. BMI is associated with mammographic density measures, but the role of circulating sex hormone concentrations is less clear. We investigated the relationship between BMI, circulating sex hormone concentrations, and mammographic density phenotypes using Mendelian randomization (MR).

Methods: We applied two-sample MR approaches to assess the association between genetically predicted circulating concentrations of sex hormones [estradiol, testosterone, sex hormone-binding globulin (SHBG)], BMI, and mammographic density phenotypes (dense and non-dense area). We created instrumental variables from large European ancestry-based genome-wide association studies and applied estimates to mammographic density phenotypes in up to 14,000 women of European ancestry. We performed analyses overall and by menopausal status.

Results: Genetically predicted BMI was positively associated with non-dense area (IVW: β = 1.79; 95% CI = 1.58, 2.00; p = 9.57 × 10^-63) and inversely associated with dense area (IVW: β = - 0.37; 95% CI = - 0.51,- 0.23; p = 4.7 × 10^-7). We observed weak evidence for an association of circulating sex hormone concentrations with mammographic density phenotypes, specifically inverse associations between genetically predicted testosterone concentration and dense area (β = - 0.22; 95% CI = - 0.38, - 0.053; p = 0.009) and between genetically predicted estradiol concentration and non-dense area (β = - 3.32; 95% CI = - 5.83, - 0.82; p = 0.009), although results were not consistent across a range of MR approaches.

Conclusion: Our findings support a positive causal association between BMI and mammographic non-dense area and an inverse association between BMI and dense area. Evidence was weaker and inconsistent for a causal effect of circulating sex hormone concentrations on mammographic density phenotypes. Based on our findings, associations between circulating sex hormone concentrations and mammographic density phenotypes are weak at best.

Keywords: BMI; Breast density; Causal inference; Mendelian randomization; Sex hormones.

Grants and funding

CA244670/Division of Cancer Epidemiology and Genetics, National Cancer Institute