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Using human genetics to understand the disease impacts of testosterone in men and women

Nature Medicine volume 26, pages 252–258 (2020)Cite this article

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Abstract

Testosterone supplementation is commonly used for its effects on sexual function, bone health and body composition, yet its effects on disease outcomes are unknown. To better understand this, we identified genetic determinants of testosterone levels and related sex hormone traits in 425,097 UK Biobank study participants. Using 2,571 genome-wide significant associations, we demonstrate that the genetic determinants of testosterone levels are substantially different between sexes and that genetically higher testosterone is harmful for metabolic diseases in women but beneficial in men. For example, a genetically determined 1 s.d. higher testosterone increases the risks of type 2 diabetes (odds ratio (OR) = 1.37 (95% confidence interval (95% CI): 1.22–1.53)) and polycystic ovary syndrome (OR = 1.51 (95% CI: 1.33–1.72)) in women, but reduces type 2 diabetes risk in men (OR = 0.86 (95% CI: 0.76–0.98)). We also show adverse effects of higher testosterone on breast and endometrial cancers in women and prostate cancer in men. Our findings provide insights into the disease impacts of testosterone and highlight the importance of sex-specific genetic analyses.

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Fig. 1: Cluster analysis of male identified sex hormone signals.
Fig. 2: Cluster analysis of female identified sex hormone signals.
Fig. 3: Plots showing the odds of T2D and PCOS per unit higher testosterone and SHBG using genetic instruments in MR analyses.
Fig. 4: Plots showing the odds of cancer per unit higher testosterone and SHBG using genetic instruments in MR analyses.

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Data availability

All data used in discovery analyses are available from UK Biobank upon request (https://www.ukbiobank.ac.uk).

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Acknowledgements

This research has been conducted using the UK Biobank resource under application numbers 9072, 9055, 9797 and 44448. The authors acknowledge the use of the University of Exeter High-Performance Computing facility in carrying out this work. We thank K. Patel, R. Andrews and T. McDonald for their advice on the derivation of testosterone measures and their roles in PCOS and diabetes. We thank the MAGIC consortium and I. Barroso for sharing prepublication sex-specific glycemic trait GWAS data. A.R.W. and T.M.F. are supported by the European Research Council grant no. SZ-245 50371-GLUCOSEGENES-FP7-IDEAS-ERC. R.N.B. is funded by the Wellcome Trust and Royal Society grant no. 104150/Z/14/Z. J.T. is supported by the Academy of Medical Sciences Springboard award which is supported by the Wellcome Trust and GCRF (grant no. SBF004\1079). This work was supported by the Medical Research Council (Unit Programme numbers MC_UU_12015/1 and MC_UU_12015/2).

Author information

Author notes

  1. Mark I McCarthy

    Present address: Genentech, San Francisco, CA, USA

  2. These authors contributed equally: Katherine S. Ruth, Felix R. Day, Jessica Tyrrell, Deborah J. Thompson, Anna Murray, Ken K. Ong, Timothy M. Frayling, John R. B. Perry.

  3. A list of members and affiliations appears in the Supplementary Information.

Authors and Affiliations

  1. University of Exeter Medical School, University of Exeter, Exeter, UK

    Katherine S Ruth, Jessica Tyrrell, Andrew R Wood, Robin N Beaumont, Susan Martin, Anna Murray & Timothy M Frayling

  2. Medical Research Council (MRC) Epidemiology Unit, University of Cambridge, Cambridge, UK

    Felix R Day, Laura Wittemans, Alexander S. Busch, A. Mesut Erzurumluoglu, Benjamin Hollis, Claudia Langenberg, Nicholas J Wareham, Ken K Ong & John R. B. Perry

  3. Centre for Cancer Genetic Epidemiology, University of Cambridge, Cambridge, UK

    Deborah J Thompson & Douglas F Easton

  4. Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK

    Anubha Mahajan, Laura Wittemans & Mark I McCarthy

  5. International Center for Research and Research Training in Endocrine Disruption of Male Reproduction and Child Health, University of Copenhagen, Copenhagen, Denmark

    Alexander S. Busch

  6. Department of Growth and Reproduction, University of Copenhagen, Copenhagen, Denmark

    Alexander S. Busch

  7. Department of Genetics, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia

    Tracy A. O’Mara

  8. Oxford Centre for Diabetes, University of Oxford, Oxford, UK

    Mark I McCarthy

  9. Oxford NIHR Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK

    Mark I McCarthy

  10. MRC Biostatistics Unit, University of Cambridge, Cambridge, UK

    Stephen Burgess

  11. BHF Cardiovascular Epidemiology Unit, University of Cambridge, Cambridge, UK

    Stephen Burgess

  12. Department of Paediatrics, University of Cambridge, Cambridge, UK

    Ken K Ong

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  1. Katherine S Ruth
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The Endometrial Cancer Association Consortium

Contributions

K.S.R., F.R.D., J.T., D.J.T., J.R.B.P. and T.M.F. analyzed the data. K.S.R., F.R.D., J.T., J.R.B.P., T.M.F., K.K.O. and A. Murray drafted the manuscript. D.J.T., A.R.W., A. Mahajan, R.N.B., L.W., S.M., A.S.B., A.M.E., B.H., T.A.O’M., M.I.M., C.L., D.F.E., N.J.W., S.B. and the ECA consortium contributed data and advised on analysis. J.R.B.P., T.M.F., K.K.O., A. Murray and S.B. designed and led the study. All co-authors commented on and revised the manuscript.

Corresponding authors

Correspondence to Timothy M Frayling or John R. B. Perry.

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Competing interests

The views expressed in this article are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health. M.I.M. has served on advisory panels for Pfizer, NovoNordisk and Zoe Global, and has received honoraria from Merck, Pfizer, Novo Nordisk and Eli Lilly, and research funding from Abbvie, Astra Zeneca, Boehringer Ingelheim, Eli Lilly, Janssen, Merck, NovoNordisk, Pfizer, Roche, Sanofi Aventis, Servier and Takeda. As of June of 2019, M.I.M. is an employee of Genentech, and a holder of Roche stock. T.M.F. holds an MRC CASE studentship with GSK and has consulted for Sanofi, Servier and Boerhinger Ingelheim.

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Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Replication of identified SHBG signals in CHARGE meta-analysis.

a) Effect size comparison performed against published estimates from the CHARGE male SHBG meta-analysis (N = 12,401). b) Effect size comparison performed against published estimates from the CHARGE female SHBG meta-analysis (N = 9,390). The SHBG cis locus (which had a concordant effect direction) has been excluded to maintain an appropriate scale.

Extended Data Fig. 2 Relationship between measured sex hormone levels in the EPIC-Norfolk study and polygenic score for increased sex hormone level.

a) Total testosterone levels in the EPIC-Norfolk study by polygenic score for increased total testosterone (n = 5,334 men; n = 3,804 women). b) SHBG levels in the EPIC-Norfolk study by polygenic score for increased SHBG (n = 5,694 men; n = 5,476 women). Bars denote the standard error around the point estimate of the mean. Effect on hormone is given in standard deviations (SDs). SHBG = sex hormone binding globulin.

Extended Data Fig. 3 LD score regression analysis of enrichment of sex hormone signals in 53 GTEx tissues and cell types.

a) Analysis in men. b) Analysis in women.

Extended Data Fig. 4 Results of inverse-variance weighted Mendelian randomization analysis of sex hormone genetic instruments on metabolic traits and body composition outcomes.

Dot plots representing the change in the following metabolic outcomes and body composition traits in males and females per unit higher sex hormone: a) Total lean mass. b) Total fat mass. c) BMI. d) Waist-hip ratio adjusted for BMI. e) Fasting insulin. f) Fasting glucose. g) Type 2 diabetes. Bars indicate 95% confidence interval around the point estimate from inverse-variance weighted analysis. Analyses are based on association statistics generated in a maximum of: total and specific testosterone, n = 194,453 men and n = 230,454 women; bioavailable testosterone, n = 178,782 men and n = 188,507 women; SHBG, n = 180,726 men and n = 189,473 women; total lean mass and total fat mass, n = 9,102 men and n = 10,406 women; BMI, n = 152,893 men and n = 171,977 women; WHR adjusted for BMI, n = 93,480 men and n = 116,742 women; fasting insulin, n = 47,806 men and n = 50,404 women; fasting glucose, n = 67,506 men and n = 73,089 women; T2D, n = 34,990 cases and n = 150,760 controls in men and n = 17,790 cases and n = 243,645 controls in women. Numbers of genetic variants included in the analyses are given in Supplementary Table 20, 21, 23 and 26. BMI = body mass index; SHBG = sex hormone binding globulin; T = testosterone; T Specific = testosterone cluster; WHR = waist-hip ratio.

Extended Data Fig. 5 Results of Mendelian randomization analysis in men of genetic instruments for testosterone and SHBG on the outcome of Type 2 diabetes.

Plots show effect on ln(odds) of Type 2 diabetes (y axes) in men of the following sex hormone genetic instruments (x axes; effect size in units). a) Total testosterone. b) Steiger filtered total testosterone. c) Bioavailable testosterone. d) Steiger filtered bioavailable testosterone. e) Testosterone specific cluster. f) Steiger filtered testosterone specific cluster. g) SHBG. h) Steiger filtered SHBG. i) SHBG specific cluster. j) Steiger filtered SHBG specific cluster. P-values and effect size estimates (indicated by lines) are from Egger (pink), IVW (blue), and median IV (red) Mendelian randomization analyses. Bars indicate 95% confidence interval around the point estimate for each genetic variant. Analyses are based on association statistics generated in a maximum of: total testosterone (including specific and Steiger filtered), n = 194,453; bioavailable testosterone (including Steiger filtered), n = 178,782; SHBG (including specific and Steiger filtered), n = 180,726; T2D, n = 34,990 cases and n = 150,760 controls. Numbers of genetic variants included in the analyses are given in Supplementary Table 20. SHBG = sex hormone binding globulin.

Extended Data Fig. 6 Results of inverse-variance weighted Mendelian randomization analysis of sex hormone genetic instruments on cancer outcomes.

Dot plots representing the change in the odds of the following cancers per unit higher sex hormone in males or females, as appropriate. a) Prostate cancer in males. b) Breast cancer (all types) and estrogen receptor positive (ER + ) and negative (ER-) subtypes in females. c) Endometrial cancer in females. d) Ovarian cancer in females. Bars indicate 95% confidence interval around the point estimate from inverse-variance weighted analyses. Analyses are based on association statistics generated in a maximum of: total and specific testosterone, n = 194,453 men and n = 230,454 women; bioavailable testosterone, n = 178,782 men and n = 188,507 women; SHBG, n = 180,726 men and n = 189,473 women; estradiol, n = 206,927 men; prostate cancer, 67,158 cases and 48,350 controls; breast cancer, n = 105,974 cases and n = 122,977 controls; ER negative subtype breast cancer, n = 21,468 cases and n = 100,594 controls; ER positive subtype breast cancer, n = 69,501 cases and n = 95,039 controls; endometrial cancer, n = 12,270 cases and n = 46,126 controls; ovarian cancer, n = 25,509 cases and n = 40,941 controls. Numbers of genetic variants included in the analyses are given in Supplementary Table 25 and 27. SHBG = sex hormone binding globulin; T = testosterone; T Specific = testosterone cluster.

Extended Data Fig. 7 Results of inverse-variance weighted Mendelian randomization analysis in females of sex hormone genetic instruments on PCOS.

Dot plot represents the odds of PCOS per unit higher sex hormone. Bars indicate 95% confidence interval around the point estimate from inverse-variance weighted analyses. Analyses are based on association statistics generated in a maximum of: total and specific testosterone, n = 230,454; bioavailable testosterone, n = 188,507; SHBG, n = 189,473; PCOS, n = 10,074 cases and n = 103,164 controls. Numbers of genetic variants included in the analyses are given in Supplementary Table 21. PCOS = polycystic ovary syndrome; SHBG = sex hormone binding globulin; T = testosterone; T Specific = testosterone cluster.

Extended Data Fig. 8 Results of Mendelian randomization analysis in women of genetic instruments for testosterone and SHBG on the outcome of PCOS.

Plots show effect on ln(odds) of PCOS (y axes) of the following sex hormone genetic instruments in women (x axes; effect size in units). a) Total testosterone. b) Steiger filtered total testosterone. c) Bioavailable testosterone. d) Steiger filtered bioavailable testosterone. e) Testosterone specific cluster. f) Steiger filtered testosterone specific cluster. g) SHBG. h) Steiger filtered SHBG. i) SHBG specific cluster. j) Steiger filtered SHBG specific cluster. P-values and effect size estimates (indicated by lines) are from Egger (pink), IVW (blue), and median IV (red) Mendelian randomization analyses. Bars indicate 95% confidence interval around the point estimate for each genetic variant. Analyses are based on association statistics generated in a maximum of: total testosterone (including specific and Steiger filtered), n = 230,454; bioavailable testosterone (including Steiger filtered), n = 188,507; SHBG (including specific and Steiger filtered), n = 189,473; PCOS, n = 10,074 cases and n = 103,164 controls. Numbers of genetic variants included in the analyses are given in Supplementary Table 21. PCOS = polycystic ovary syndrome; SHBG = sex hormone binding globulin.

Extended Data Fig. 9 Results of Mendelian randomization analysis in women of genetic instruments for testosterone and SHBG on the outcome of Type 2 diabetes.

Plots show effect on ln(odds) of Type 2 diabetes in women (y axes) of the following sex hormone genetic instruments in women (x axes; effect size in units). a) Total testosterone. b) Steiger filtered total testosterone. c) Bioavailable testosterone. d) Steiger filtered bioavailable testosterone. e) Testosterone specific cluster. f) Steiger filtered testosterone specific cluster. g) SHBG. h) Steiger filtered SHBG. i) SHBG specific cluster. j) Steiger filtered SHBG specific cluster. P-values and effect size estimates (indicated by lines) are from Egger (pink), IVW (blue), and median IV (red) Mendelian randomization analyses. Bars indicate 95% confidence interval around the point estimate for each genetic variant. Analyses are based on association statistics generated in a maximum of: total testosterone (including specific and Steiger filtered), n = 230,454; bioavailable testosterone (including Steiger filtered), n = 188,507; SHBG (including specific and Steiger filtered), n = 189,473; T2D, n = 17,790 cases and n = 243,645 controls. Numbers of genetic variants included in the analyses are given in Supplementary Table 21. SHBG = sex hormone binding globulin.

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Ruth, K.S., Day, F.R., Tyrrell, J. et al. Using human genetics to understand the disease impacts of testosterone in men and women. Nat Med 26, 252–258 (2020). https://doi.org/10.1038/s41591-020-0751-5

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