Abstract
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.
Subject terms: Cancer, Risk factors
Background
Cortisol is a glucocorticoid hormone that plays a vital role in the physiological response to endogenous and exogenous stressors in the human body. While abnormally high or low cortisol levels are presenting features of endocrinologic clinical syndromes [1], the long-term effects of moderate but persistent alterations in cortisol levels are less clear.
Some evidence, however, suggests that cortisol might be involved in cancer development. Cortisol’s immunosuppressive effect may result in reduced immunosurveillance of early-stage cancer, facilitating their immune escape and acquisition of further oncogenic mutations [2, 3]. Additionally, cortisol has obesogenic and hyperglycaemic effects [4], and both weight gain and insulin resistance are associated with increased risk for a range of malignancies [5, 6]. Finally, exposure to physiological stress has been proposed to increase cancer risk [2, 7, 8], although observational studies conducted to date have provided inconclusive evidence [2, 7, 9, 10]. These inconsistent results may be related to heterogeneous classification of stress (work stress, stressful life events, etc.), confounding, reverse causality, different cancer outcomes, or the play of chance. Epidemiological data on cortisol per se in relation to risk of cancer are scarce.
We conducted a Mendelian randomisation (MR) study to investigate the potential causal association between plasma cortisol levels and risk of overall and common site-specific cancers. Results of this MR study may provide evidence as to whether prolonged and moderate hypercortisolaemia may increase cancer risk.
Methods
Study design and MR assumptions
The present study is based on the two-sample MR design with genetic association estimates for plasma cortisol obtained from one study and the corresponding estimates for cancer obtained from other studies. MR is an instrumental variable analysis that builds upon three principal assumptions: (1) the genetic variants used as instrumental variables must be strongly associated with the exposure, (2) the genetic variants must not be associated with any confounders of the exposure–outcome relationship, and (3) the genetic variants must be associated with the outcome through the exposure and not through any alternative causal pathway (Fig. 1).
Fig. 1. A directed acyclic graph representing the MR framework, with the present MR study as an example.
The dashed lines represent pathways that would violate the MR assumptions.
Genetic instrument
As instrumental variables, we used three partially correlated single-nucleotide polymorphisms (SNPs) strongly associated with morning plasma cortisol levels (P < 5 × 10−8) in a meta-analysis of genome-wide association studies (GWAS) including 12,597 participants of European ancestries and that were replicated in an independent sample of 2795 participants [11]. The three SNPs, rs11621961, rs12589136, and rs2749527, were in or near genes highly expressed in tissues associated with cortisol physiology, including SERPINA6 (encoding corticosteroid binding globulin) and SERPINA1 (encoding α1-antitrypsin that inhibits cleavage of the reactive centre loop that releases cortisol from corticosteroid-binding globulin) [11, 12]. Together the SNPs explained 0.54% of the variance in plasma cortisol [11].
Data sources for cancer
Summary-level GWAS data for site-specific cancers in individuals of European ancestries were obtained from the Breast Cancer Association Consortium [13], a GWAS meta-analysis of endometrial cancer [14], the Ovarian Cancer Association Consortium [15], the Prostate Cancer Association Group to Investigate Cancer Associated Alterations in the Genome consortium [16], the International Lung Cancer Consortium [17], and the FinnGen consortium (for overall and site-specific cancers) [18]. We additionally used data from the UK Biobank [19] for overall and common site-specific cancers except endometrial cancer as UK Biobank was included in the GWAS meta-analysis of endometrial cancer [14]. For UK Biobank, we performed standard genetic and individual-level quality control procedures as described previously [20], that is, the analysis was restricted to SNPs with call rate ≥99%, info score >0.9, Hardy–Weinberg equilibrium P value ≥10−5. Moreover, we excluded non-European ancestries (self-report or inferred by genetics), genetic sex mismatch, or excess heterozygosity (>3 standard deviations from the mean). We included only one of each set of related participants (third-degree relatives or closer). The analyses of overall cancer, colorectal cancer, lung cancer, and malignant melanoma included 367,561 individuals. The analyses of breast and ovarian cancer were conducted in women only (n = 198,825), and the analysis of prostate cancer was conducted in men only (n = 168,736). Genetic associations were estimated using snptest [21] with adjustment for age, sex, and ten genomic principal components. Cancer cases were defined based on data from the national UK cancer registry, electronic health records, hospital episode statistics data, death certification data, and self-reported information, verified by interview with a nurse.
Ethical approval and informed consent from participants had previously been obtained [13–19]. The present MR analyses were approved by the Swedish Ethical Review Authority.
Power calculation
A power calculation was performed using an online tool [22]. We set the significance level at 0.05 and assumed that the genetic instrument explained 0.54% of the variance in plasma cortisol [11]. Due to the lack of previous studies explicitly measuring the association between cortisol and cancer, we averaged the three significant associations reported in a recent meta-analysis of observational studies investigating the potential effect of work-related stress on various cancers [9], which resulted in an anticipated odds ratio (OR) of 1.6. To ensure sufficient power, we only included site-specific cancers for which we had at least 80% power to detect an OR of 1.6 in the meta-analysis of all data sources. We also performed power calculations under different anticipated ORs (Table S1), which showed at least 80% power for most cancers while assuming an OR of ≥1.3 and lower power for weaker associations.
Statistical analysis
The two-sample summary-level MR analyses were performed using the fixed-effects inverse-variance weighted method with correction for the correlations between SNPs by specifying the correlation matrix in the MendelianRandomization package in R [23]. The correlation between SNPs was estimated in 367,643 unrelated UK Biobank participants of European ancestries. A leave-one-out analysis was conducted for any observed association between genetically predicted plasma cortisol and cancer. As cortisol has obesogenic and hyperglycaemic effects [4], we used multivariable MR analysis to investigate whether any observed association may be mediated by body mass index, glucose, or insulin. Additionally, multivariable MR analysis was performed to assess whether the cortisol–cancer association may be mediated by sex hormone-binding globulin (SHBG), oestradiol, or testosterone. We obtained summary-level data for body mass index from meta-analyses of the Genetic Investigation of ANthropometric Traits consortium and UK Biobank [24]; for fasting glucose and fasting insulin from the Meta-Analyses of Glucose and Insulin-related traits consortium [25]; and for SHBG, oestradiol, and testosterone from Neale Laboratory based on analyses of women in UK Biobank [26]. Fixed-effects inverse-variance weighted meta-analysis was used to combine the MR estimates from different data sources for each cancer. To account for multiple testing, we used a Bonferroni-corrected threshold of P <0.05/8 = 0.006 for statistical significance.
Results
The statistical power in the analysis of each cancer is presented in Table S1. The cancer sites for which we had 80% power to detect an OR of ≥1.6 are presented in Fig. 2. The summary statistics data for the associations of the three SNPs with plasma cortisol and each cancer are available in Table S2, and the linkage disequilibrium and correlations between SNPs are shown in Table S3.
Fig. 2. Associations of genetically predicted plasma cortisol with risk of overall and common site-specific cancers.
The estimates are scaled per one standard deviation increment in plasma cortisol and were derived from the fixed-effects inverse-variance weighted method adjusted for the correlations between genetic variants. BCAC Breast Cancer Association Consortium, CI confidence interval, EC endometrial cancer, ILCCO International Lung Cancer Consortium, OCAC Ovarian Cancer Association Consortium, OR odds ratio, PRACTICAL Prostate Cancer Association Group to Investigate Cancer Associated Alterations in the Genome consortium.
Higher genetically predicted plasma cortisol levels were associated with an increased risk of endometrial cancer but not with other common site-specific cancers or overall cancer (Fig. 2). The OR of endometrial cancer was 1.50 (95% confidence interval [CI] 1.13–1.99) per one standard deviation increase of plasma cortisol in the meta-analysis of the two data sources, with strength of evidence (P = 0.005) below our Bonferroni-corrected threshold. Using data from the GWAS meta-analysis of endometrial cancer that provided data on histological subtypes of endometrial cancer, the corresponding ORs were 1.60 (95% CI 1.13–2.26) for the endometrioid subtype (n = 8758 cases) and 1.18 (95% CI 0.51–2.76) for the non-endometrioid subtype (n = 1230 cases). In leave-one-out analysis and meta-analysis of the two data sources, the ORs of overall endometrial cancer per one standard deviation increment of plasma cortisol were 1.49 (95% CI 1.10–2.03), 1.59 (95% CI 1.15–2.16), and 1.47 (95% CI 1.10–1.96) after omitting rs11621961, rs12589136, and rs2749527, respectively. In multivariable MR analysis, adjustment for genetically predicted body mass index, fasting glucose, or fasting insulin did not appreciably change the association between genetically predicted plasma cortisol with overall endometrial cancer (Fig. S1). Adjustment for genetically predicted SHBG but not oestradiol or testosterone levels attenuated the OR estimate; however, the precision of the estimates was low, particularly in the analyses based on FinnGen (Fig. S1).
Discussion
In the present study, we used MR to assess the potential role of cortisol in cancer development in humans. Because genetic variants are randomly assigned at conception, mimicking randomisation in a clinical trial, confounding from environmental exposures can be diminished. Furthermore, the likelihood of reverse causality can be reduced because genetic variants are fixed at conception. Our findings showed that a standard deviation increase of genetically predicted plasma cortisol was associated with an overall 50% higher odds of endometrial cancer in a meta-analysis of two independent study samples. The association appeared to be stronger for the endometrioid histological subtype, a hormonally driven type of cancer, than for the non-endometrioid histological subtype of endometrial cancer. There was no evidence of a causal association of plasma cortisol with other common site-specific cancers or overall cancer.
The potential mechanism underlying the association between elevated plasma cortisol and increased risk of endometrial cancer remains to be elucidated, and we have prioritised some research hypotheses. First, elevated levels of oestrogen and testosterone increase endometrial cancer risk [27, 28], and the bioactive concentrations of these hormones are influenced by SHBG. Excessive cortisol levels in Cushing’s disease are associated with increased testosterone levels and decreased SHBG levels [29]. Furthermore, oestrogen and cortisol exert complex feedback on each other’s levels [30–32]. An experimental study showed that expression of the receptor for cortisol and other glucocorticoids was associated with poor prognosis in endometrioid-type endometrial tumours with high oestrogen receptor expression [33]. We found some evidence that the association between genetically predicted plasma cortisol and endometrial cancer risk was attenuated after adjustment for genetically predicted SHBG but not oestradiol or testosterone. However, these multivariable MR analyses resulted in unstable estimates as reflected by broad CIs, and it is challenging to infer mediation in multivariable MR analysis with few genetic instruments. Additionally, the genetic association estimates for these hormones may not be fully reliable as hormonal levels are strongly influenced by menstrual cycle and menopausal status, and the estimates were based on data from women at any menstrual cycle and age. Hence, these hormonal-adjusted results should be interpreted with caution. Second, the association between plasma cortisol and endometrial cancer might also be mediated by obesity, which is strongly associated with an increased risk of endometrial cancer [6, 14], particularly the endometrioid subtype [14]. Nevertheless, the association between genetically predicted plasma cortisol and endometrial cancer risk did not change essentially after adjustment for genetically predicted body mass index, suggesting that the association is unlikely to be mediated by obesity. Finally, additional mediators might be hyperglycaemia and hyperinsulinaemia, which have been shown to be implicated in endometrial cancer aetiopathogenesis [6, 34]. The association, however, did not change materially after adjustment for genetically predicted fasting glucose or insulin. Further research is necessary to elucidate the potential mechanism linking hypercortisolaemia to endometrial cancer development.
A limitation of this study is that the genetic variants explained a relatively small proportion of the variance of plasma cortisol levels (0.54%). Thus, despite large number of cancer cases and sample sizes, the statistical power was relatively limited, and we cannot rule out that we may have overlooked weak associations between plasma cortisol and certain cancers. Conversely, it is not yet clear whether plasma cortisol levels as determined by mechanisms other than those controlled by the SERPINA1/SERPINA6 locus influence endometrial cancer risk or whether the association that we observed is specific to this locus. Another shortcoming is that our study samples primarily comprised individuals of European ancestries, and therefore, our findings might not be generalisable to other populations. Larger and more diverse GWAS of plasma cortisol levels are required to further elucidate the genetic architecture of this trait and its relationship with cancer risk. Finally, as this study was based on genetic instruments for cortisol, our results are not directly comparable to observational findings on physiological stress [2, 7, 9, 10], which may influence cancer risk through other mechanisms than via cortisol. Our findings are, however, generally consistent with these studies, with point estimates in the directions of increased risk for various cancers. This suggests the need for further research to generate more powerful genetic instruments for cortisol and to assess whether cortisol mediates observational associations between psychological stress and cancer.
In conclusion, these results indicate that elevated plasma cortisol levels may increase the risk of endometrial cancer but are not strongly associated with risk of other common cancers or overall cancer. The mechanism by which this occurs is unclear and warrants further investigation.
Supplementary information
Acknowledgements
The authors would like to thank the investigators of the Breast Cancer Association Consortium (BCAC), Endometrial Cancer Association Consortium (ECAC), Genetic Investigation of ANthropometric Traits (GIANT) consortium, International Lung Cancer Consortium (ILCCO), Meta-Analyses of Glucose and Insulin-related traits consortium (MAGIC), Ovarian Cancer Association Consortium (OCAC), Prostate Cancer Association Group to Investigate Cancer Associated Alterations in the Genome (PRACTICAL) consortium, and the FinnGen consortium for sharing summary-level GWAS data. Analyses of UK Biobank data were performed under application 29202.
Author contributions
SCL had full access to the data. SCL, SB, and EA designed the study. SCL performed the statistical analyses and created the figure. SCL, W-HL, and EA drafted the manuscript. SCL, W-HL, SK, SB, and EA interpreted the data and edited the manuscript. All authors have given final approval of the version to be published.
Funding
SCL acknowledges research support from the Swedish Research Council (Vetenskapsrådet, 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.
Data availability
All data used in this study are publicly available summary-level data, with the relevant studies cited.
Competing interests
The authors declare no competing interests.
Ethics approval and consent to participate
Ethics approval and consent to participate had been obtained. The present analyses were approved by the Swedish Ethical Review Authority. The study was performed in accordance with the Declaration of Helsinki.
Footnotes
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary information
The online version contains supplementary material available at 10.1038/s41416-021-01505-8.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
All data used in this study are publicly available summary-level data, with the relevant studies cited.