José M Huerta1,2, Antonio J Molina3,4, María Dolores Chirlaque1,2,5, Pedro Yepes1, Ferrán Moratalla-Navarro2,6, Víctor Moreno2,6,7, Pilar Amiano2,8, Marcela Guevara2,9,10, Conchi Moreno-Iribas9,10,11, Javier Llorca2,12, Guillermo Fernández-Tardón2,13,14, Ana Molina-Barceló15, Juan Alguacil2,16, Rafael Marcos-Gragera2,17,18, Gemma Castaño-Vinyals2,19,20,21, Beatriz Pérez-Gómez2,22,23, Manolis Kogevinas2,19,20,21, Marina Pollán2,22,23, Vicente Martín2,24. 1. Department of Epidemiology, Murcia Regional Health Council, IMIB-Arrixaca, Ronda de Levante 11, 30008, Murcia, Spain. 2. CIBER Epidemiología Y Salud Pública (CIBERESP), Av. Monforte de Lemos, 3-5, 28029, Madrid, Spain. 3. Grupo de Investigación en Interacciones Gen-Ambiente Y Salud (GIIGAS), Instituto de Biomedicina (IBIOMED), Universidad de León, Campus de Vegazana, s/n, 24071, León, Spain. ajmolt@unileon.es. 4. Área de Medicina Preventiva y Salud Pública. Departamento de Ciencias Biomédicas, Universidad de León, Campus de Vegazana, s/n, 24071, León, Spain. ajmolt@unileon.es. 5. Departamento de Ciencias Sociosanitarias, Facultad de Medicina, Universidad de Murcia, Campus de Espinardo, 30100, Murcia, Spain. 6. Oncology Data Analytics Program (ODAP), Catalan Institute of Oncology and Oncobell Program, Bellvitge Biomedical Research Institute (IDIBELL), Gran Via de L'Hospitalet, 199, 08908, Hospitalet de Llobregat, Spain. 7. Department of Clinical Sciences, Faculty of Medicine, University of Barcelona, Feixa Llarga, s/n, 08907, Hospitalet de Llobregat, Spain. 8. Public Health Division of Gipuzkoa, Biodonostia Research Institute, Avda. de Navarra, 4, 20013, Donostia-San Sebastián, Spain. 9. Navarra Public Health Institute, Calle Leyre, 15, 31003, Pamplona, Spain. 10. IdiSNA, Navarra Institute for Health Research, C/ Irunlarrea, 3, 31008, Pamplona, Spain. 11. REDISSEC, Red de Investigación en Servicios de Salud en Enfermedades Crónicas, Madrid, Spain. 12. Facultad de Medicina, Universidad de Cantabria, Av. Cardenal Herrera Oria, s/n, 39011, Santander, Spain. 13. Instituto Universitario de Oncología, Universidad de Oviedo, C/ Julián Clavería, s/n, 33006, Oviedo, Spain. 14. Instituto de Investigación Sanitaria del Principado de Asturias (ISPA), Av. Roma, s/n, 33011, Oviedo, Spain. 15. Cancer and Public Health Area, FISABIO-Public Health, Avda. de Catalunya, 21, 46020, Valencia, Spain. 16. Centro de Investigación en Recursos Naturales, Salud Y Medio Ambiente (RENSMA), Universidad de Huelva, Avda. de Las Fuerzas Armadas, s/n, 21007, Huelva, Spain. 17. Epidemiology Unit and Girona Cancer Registry. Oncology Coordination Plan, Department of Health, Autonomous Government of Catalonia, Catalan Institute of Oncology, Av. França, s/n, 17007, Girona, Spain. 18. Descriptive Epidemiology, Genetics and Cancer Prevention Group, Biomedical Research Institute (IDIBGI), C/ Dr. Castany, s/n, 17190, Salt, Spain. 19. ISGlobal, C/ Rosselló, 132, 08036, Barcelona, Spain. 20. IMIM (Hospital del Mar Medical Research Institute), C/ Doctor Aiguader, 88, 08003, Barcelona, Spain. 21. Universitat Pompeu Fabra (UPF), Plaza de La Merced, 10, 08002, Barcelona, Spain. 22. Environmental and Cancer Epidemiology Department, National Centre of Epidemiology-Instituto de Salud Carlos III, C/ Melchor Fernández Almagro, 5, 28029, Madrid, Spain. 23. Oncology and Hematology Area, IIS Puerta De Hierro, Cancer Epidemiology Research Group, C/ Manuel de Falla, 1, 28222, Majadahonda, Madrid, Spain. 24. Grupo de Investigación en Interacciones Gen-Ambiente Y Salud (GIIGAS), Instituto de Biomedicina (IBIOMED), Universidad de León, Campus de Vegazana, s/n, 24071, León, Spain.
Abstract
PURPOSE: Literature on the separate effects of physical activities (PA) on risk of breast cancer (BC) sub-types is heterogeneous. We investigated domain-specific associations between PA and BC risk by menopausal status and molecular subtype. METHODS: 1389 histologically confirmed invasive BC cases and 1712 controls from the MCC-Spain study were included (age: 20-85 years). Questionnaire information on PA at work, at home, and during leisure time, including recreational PA and sedentary time, and data on reproductive history, anthropometry, family history of BC, diet, and lifestyles were obtained through face-to-face interviews. Information on the expression of oestrogen (ER), progesterone (PR), and HER2 receptors was available for > 95% of the cases. Mixed-effects multivariable logistic regression models were used to estimate odds ratios (OR) of BC sub-types. RESULTS: Occupational PA (OPA) intensity was associated with higher BC risk. Associations were stronger for pre-menopausal (ORactive/very active vs. sedentary job 1.89; 95% confidence interval (CI) 1.22, 2.91) and ER+/PR+, HER2- tumours (OR 1.80; 95% CI 1.28, 2.53). Sedentary time was associated with higher risk of post-menopausal BC (OR6-9 vs. <3 h/day 1.69; 95% CI 1.22, 2.32). Moderate-to-high-intensity household (HPA) and recreational PA (RPA) were inversely associated with BC occurrence in pre- and post-menopausal women, with estimated 14-33% lower risks (P for trend < 0.001) above 1000 MET·min/week. CONCLUSIONS: Higher levels of HPA and RPA were associated with lower risk of BC, with heterogeneity by molecular type, whereas sitting time was a consistent independent risk factor of BC risk. The positive association found for OPA with ER+/PR+ BC deserves further investigation.
PURPOSE: Literature on the separate effects of physical activities (PA) on risk of breast cancer (BC) sub-types is heterogeneous. We investigated domain-specific associations between PA and BC risk by menopausal status and molecular subtype. METHODS: 1389 histologically confirmed invasive BC cases and 1712 controls from the MCC-Spain study were included (age: 20-85 years). Questionnaire information on PA at work, at home, and during leisure time, including recreational PA and sedentary time, and data on reproductive history, anthropometry, family history of BC, diet, and lifestyles were obtained through face-to-face interviews. Information on the expression of oestrogen (ER), progesterone (PR), and HER2 receptors was available for > 95% of the cases. Mixed-effects multivariable logistic regression models were used to estimate odds ratios (OR) of BC sub-types. RESULTS: Occupational PA (OPA) intensity was associated with higher BC risk. Associations were stronger for pre-menopausal (ORactive/very active vs. sedentary job 1.89; 95% confidence interval (CI) 1.22, 2.91) and ER+/PR+, HER2- tumours (OR 1.80; 95% CI 1.28, 2.53). Sedentary time was associated with higher risk of post-menopausal BC (OR6-9 vs. <3 h/day 1.69; 95% CI 1.22, 2.32). Moderate-to-high-intensity household (HPA) and recreational PA (RPA) were inversely associated with BC occurrence in pre- and post-menopausal women, with estimated 14-33% lower risks (P for trend < 0.001) above 1000 MET·min/week. CONCLUSIONS: Higher levels of HPA and RPA were associated with lower risk of BC, with heterogeneity by molecular type, whereas sitting time was a consistent independent risk factor of BC risk. The positive association found for OPA with ER+/PR+ BC deserves further investigation.
Entities:
Keywords:
Breast cancer; Case–control study; Hormone receptors; MCC-Spain; Physical activity