Background Cortisol’s immunosuppressive, obesogenic, and hyperglycaemic effects suggest that it may play a role in cancer development. However, whether cortisol increases cancer risk is not known. We investigated the potential causal association between plasma cortisol and risk of overall and common site-specific cancers using Mendelian randomisation. Methods Three genetic variants associated with morning plasma cortisol levels at the genome-wide significance level (P < 5 × 10−8) in the Cortisol Network consortium were used as genetic instruments. Summary-level genome-wide association study data for the cancer outcomes were obtained from large-scale cancer consortia, the UK Biobank, and the FinnGen consortium. Two-sample Mendelian randomisation analyses were performed using the fixed-effects inverse-variance weighted method. Estimates across data sources were combined using meta-analysis. Results A standard deviation increase in genetically predicted plasma cortisol was associated with increased risk of endometrial cancer (odds ratio 1.50, 95% confidence interval 1.13–1.99; P = 0.005). There was no significant association between genetically predicted plasma cortisol and risk of other common site-specific cancers, including breast, ovarian, prostate, colorectal, lung, or malignant skin cancer, or overall cancer. Conclusions These results indicate that elevated plasma cortisol levels may increase the risk of endometrial cancer but not other cancers. The mechanism by which this occurs remains to be investigated.
Bibliographical noteFunding Information:
SCL acknowledges research support from the Swedish Research Council (Vetens-kapsrådet, 2016-01042 and 2019-00977), the Swedish Research Council for Health, Working Life and Welfare (Forte, 2018-00123), and the Swedish Heart-Lung Foundation (Hjärt-Lungfonden, 20190247). SK is supported by United Kingdom Research and Innovation Future Leaders Fellowship (MR/T043202/1). SB is supported by Sir Henry Dale Fellowship jointly funded by the Wellcome Trust and the Royal Society (204623/Z/16/Z). During the conduction of this study, EA was supported by the EU/EFPIA Innovative Medicines Initiative Joint Undertaking BigData@Heart grant no. 116074 and is currently funded by the British Heart Foundation Programme Grant RG/18/13/33946. This work was supported by core funding from: the UK Medical Research Council (MR/L003120/1), the British Heart Foundation (RG/13/13/30194; RG/ 18/13/33946), and the NIHR Cambridge Biomedical Research Centre (BRC-1215-20014)*. This work was also supported by Health Data Research UK, which is funded by the UK Medical Research Council, Engineering and Physical Sciences Research Council, Economic and Social Research Council, Department of Health and Social Care (England), Chief Scientist Office of the Scottish Government Health and Social Care Directorates, Health and Social Care Research and Development Division (Welsh Government), Public Health Agency (Northern Ireland), British Heart Foundation, and Wellcome. *The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care.
© 2021, The Author(s), under exclusive licence to Springer Nature Limited.
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