Chi Gao1, Chirag J Patel2, Kyriaki Michailidou3, Ulrike Peters4, Jian Gong4, Joellen Schildkraut5, Fredrick R Schumacher6, Wei Zheng7, Paolo Boffetta8, Isabelle Stucker9, Walter Willett10, Stephen Gruber11, Douglas F Easton12, David J Hunter13, Thomas A Sellers14, Christopher Haiman6, Brian E Henderson6, Rayjean J Hung15, Christopher Amos16, Brandon L Pierce17, Sara Lindström1, Peter Kraft. 1. Program in Genetic Epidemiology and Statistical Genetics, Harvard T.H. Chan School of Public Health, Boston, MA, USA. 2. Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA. 3. Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK, Department of Electron Microscopy/Molecular Pathology, Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus and. 4. Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA. 5. Cancer Prevention, Detection & Control Research Program, Duke Cancer Institute, Durham, NC, USA, Department of Public Health Sciences, University of Virginia, Charlottesville, VA, USA. 6. Department of Preventive Medicine, Keck School of Medicine, University of Southern California/Norris Comprehensive Cancer Center, Los Angeles, CA, USA. 7. Department of Medicine, Vanderbilt-Ingram Cancer Center, Vanderbilt University, Nashville, TN, USA. 8. Tisch Cancer institute and Institute for Transitional Epidemiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA. 9. Centre for Research in Epidemiology and Population Health, INSERM, Villejuif, France. 10. Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA, Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA. 11. USC Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, CA, USA. 12. Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK, Department of Oncology, University of Cambridge, Cambridge, UK. 13. Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA. 14. Department of Cancer Epidemiology, H. Lee Moffitt Cancer Center, Tampa, FL, USA. 15. Lunenfeld-Tanenbaum Research Institute of Mount Sinai Hospital, Toronto, ON, Canada. 16. Center for Genomic Medicine, Geisel School of Medicine, Dartmouth College, Lebanon, NH, USA. 17. Department of Public Health Studies, University of Chicago, Chicago, IL, USA.
Abstract
BACKGROUND: Adiposity traits have been associated with risk of many cancers in observational studies, but whether these associations are causal is unclear. Mendelian randomization (MR) uses genetic predictors of risk factors as instrumental variables to eliminate reverse causation and reduce confounding bias. We performed MR analyses to assess the possible causal relationship of birthweight, childhood and adult body mass index (BMI), and waist-hip ratio (WHR) on the risks of breast, ovarian, prostate, colorectal and lung cancers. METHODS: We tested the association between genetic risk scores and each trait using summary statistics from published genome-wide association studies (GWAS) and from 51 537 cancer cases and 61 600 controls in the Genetic Associations and Mechanisms in Oncology (GAME-ON) Consortium. RESULTS: We found an inverse association between the genetic score for childhood BMI and risk of breast cancer [odds ratio (OR) = 0.71 per standard deviation (s.d.) increase in childhood BMI; 95% confidence interval (CI): 0.60, 0.80; P = 6.5 × 10(-5)). We also found the genetic score for adult BMI to be inversely associated with breast cancer risk (OR = 0.66 per s.d. increase in BMI; 95% CI: 0.57, 0.77; P = 2.5 × 10(-7)), and positively associated with ovarian cancer (OR = 1.35; 95% CI: 1.05, 1.72; P = 0.017), lung cancer (OR = 1.27; 95% CI: 1.09, 1.49; P = 2.9 × 10(-3)) and colorectal cancer (OR = 1.39; 95% CI: 1.06, 1.82, P = 0.016). The inverse association between genetically predicted adult BMI and breast cancer risk remained even after adjusting for directional pleiotropy via MR-Egger regression. CONCLUSIONS: Findings from this study provide additional understandings of the complex relationship between adiposity and cancer risks. Our results for breast and lung cancer are particularly interesting, given previous reports of effect heterogeneity by menopausal status and smoking status.
BACKGROUND: Adiposity traits have been associated with risk of many cancers in observational studies, but whether these associations are causal is unclear. Mendelian randomization (MR) uses genetic predictors of risk factors as instrumental variables to eliminate reverse causation and reduce confounding bias. We performed MR analyses to assess the possible causal relationship of birthweight, childhood and adult body mass index (BMI), and waist-hip ratio (WHR) on the risks of breast, ovarian, prostate, colorectal and lung cancers. METHODS: We tested the association between genetic risk scores and each trait using summary statistics from published genome-wide association studies (GWAS) and from 51 537 cancer cases and 61 600 controls in the Genetic Associations and Mechanisms in Oncology (GAME-ON) Consortium. RESULTS: We found an inverse association between the genetic score for childhood BMI and risk of breast cancer [odds ratio (OR) = 0.71 per standard deviation (s.d.) increase in childhood BMI; 95% confidence interval (CI): 0.60, 0.80; P = 6.5 × 10(-5)). We also found the genetic score for adult BMI to be inversely associated with breast cancer risk (OR = 0.66 per s.d. increase in BMI; 95% CI: 0.57, 0.77; P = 2.5 × 10(-7)), and positively associated with ovarian cancer (OR = 1.35; 95% CI: 1.05, 1.72; P = 0.017), lung cancer (OR = 1.27; 95% CI: 1.09, 1.49; P = 2.9 × 10(-3)) and colorectal cancer (OR = 1.39; 95% CI: 1.06, 1.82, P = 0.016). The inverse association between genetically predicted adult BMI and breast cancer risk remained even after adjusting for directional pleiotropy via MR-Egger regression. CONCLUSIONS: Findings from this study provide additional understandings of the complex relationship between adiposity and cancer risks. Our results for breast and lung cancer are particularly interesting, given previous reports of effect heterogeneity by menopausal status and smoking status.
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