Men experience a gradual decline in their testosterone levels as they grow older. However, the effects of testosterone and the consequences of supplementation on the human body have been unclear.

Scientists use so-called randomized controlled trials to establish cause-and-effect and to reduce bias. In these experiments, participants are randomly assigned to a either a treatment group (that receives the intervention being tested) or a control group (that either receives an alternative intervention, a dummy or placebo, or no intervention at all).

Randomization ensures that both groups are balanced, and any resulting differences can be attributed to the treatment. However, randomized controlled trials are time-consuming and expensive, so trials of testosterone have had relatively small numbers of participants and short follow-up periods. This makes it difficult to draw conclusions about any potential effects of testosterone administration on less common diseases in men.

Now, Paré et al. investigated the effects of naturally produced testosterone using Mendelian randomization, which mimics randomized trials by exploiting the fact that parents randomly pass on their unique genetic variants to their children at conception. This random assignment of genetic variants leads to its informal namesake, “nature’s clinical trial”, and provides the ability to study cause-and-effect for any genetically determined factors, such as testosterone levels.

Paré et al. studied the long-term effects of testosterone on 22 diseases previously explored in randomized controlled trials, and hundreds of other traits and diseases that have not been investigated in any randomized controlled trials yet.

The Mendelian randomization analysis made it possible to examine the effects of lifelong naturally elevated testosterone levels on 469 traits and diseases. Paré et al. found that testosterone increased the density of bone mineral and decreased body fat. However, it also increased the risks of prostate cancer, high blood pressure, baldness and a condition affecting the spine. It also increased the number of red blood cells and decreased a marker of inflammation, which may be beneficial or detrimental depending on the context.

This shows that genetic analyses can be powerful methods to prioritize the allocation of limited resources towards investigating the most pressing clinical questions. The results of this study may help inform physicians and patients about the effects of long-term testosterone use. Ultimately, large randomized controlled trials are needed to conclusively address the cause-and-effect on these diseases.

In developed countries, rising rates of both serum testosterone level testing and therapy initiation have been observed among older male patients (Handelsman, 2013; Layton et al., 2014). In the USA alone, it is estimated 1.5–1.7% of males are prescribed testosterone (Baillargeon et al., 2018; Jasuja et al., 2017). Randomized clinical trials (RCT) have attempted to elucidate the benefits and risks of testosterone treatment (Bhasin et al., 2018a; Gagliano-Jucá and Basaria, 2019). These studies identified short-term beneficial effects on bone mineral density (BMD), sexual function, body fat and muscle mass, and anaemia; potential adverse effects on venous thrombosis and coronary artery plaque; and no effects on cognitive function, fatigue, or hemoglobin A1_c (HbA1_c) (Bhasin et al., 2018a; Gagliano-Jucá and Basaria, 2019; Mohler et al., 2018; Snyder et al., 2018). However, given the logistic and financial challenges involved in a well-powered RCT with appropriate follow-up, there is unlikely to be satisfactory evidence regarding long-term effects and risks of adverse outcomes, such as myocardial infarction (MI), stroke and cancer (Gagliano-Jucá and Basaria, 2019). Given the rates of testosterone prescription, efforts to resolve the causal effects of testosterone on health outcomes have important public health implications (Bhasin et al., 2018a).

Mendelian randomization (MR) is a technique for causal inference that leverages the random allocation of genetic variants to infer the unconfounded relationship between an exposure and outcome. Similar to the random assignment of participants to experimental groups in a RCT, genetic variants are randomly allocated at meiosis (Davies et al., 2018). For instance, if individuals genetically randomized to produce higher testosterone develop different rates of cardiovascular disease (CVD), then MR analysis supports a causal effect of testosterone on risk of CVD (Figure 1—figure supplement 1). Notably, this technique has previously replicated RCT findings, among others demonstrating causal roles for LDL cholesterol and dysglycemia on CVD risk (Holmes et al., 2015; Ross et al., 2015). Earlier MR studies investigating the effects of testosterone have demonstrated harmful effects on lipid levels but inconsistent effects on CVD, and they were limited by the small number of genetic variants (Schooling et al., 2018; Zhao et al., 2014). A recent MR study using the UK Biobank identified a large number of genetic variants associated with testosterone and found evidence for harmful effects on several types of cancers but sex-specific effects on type 2 diabetes (T2D) (Ruth et al., 2020). This study highlighted the importance of performing sex-specific analyses for testosterone, but it was focused on glycemic and oncologic traits (Ruth et al., 2020). Therefore, we sought to expand the scope of prior studies by performing a comprehensive scan of the effects of free testosterone on human disease in males.

We hypothesized that MR and genetic risk score (GRS) analyses would enable estimation of the causal effects of longstanding exposure to high levels of free testosterone on health outcomes in males. We first conducted a genome-wide association study (GWAS) for calculated free testosterone (CFT) in male participants of the UK Biobank (n = 161,268) cohort to identify genetic determinants of free testosterone levels. Then, using MR, we investigated the causal effects of lifelong genetically-elevated free testosterone levels on a priori health outcomes previously investigated in RCTs of testosterone treatment, encompassing: expected clinical benefits (physical activity, strength, fat-free body mass, body fat, BMD, dementia, depression) and potential adverse effects (androgenic alopecia, heematocrit, T2D, prostate cancer, benign prostate hyperplasia, blood pressure, CVD, heart failure, ischemic stroke) (Figure 1; Bhasin et al., 2018a; Gagliano-Jucá and Basaria, 2019; Mohler et al., 2018; Snyder et al., 2018). Finally, we used GRS to investigate the associations of lifelong genetically-elevated free testosterone levels on 439 health outcomes, encompassing diseases (n = 415) and biomarkers of health (n = 24) (Figure 1).

Figure 1 with 6 supplements see all

Download asset Open asset

We herein perform MR and GRS analyses of CFT to identify effects of endogenous free testosterone in males on 461 health outcomes. All effects are reported in terms of 0.1 nmol/L of CFT to approximate expected effect sizes after initiation of testosterone treatment (Bhasin et al., 2018b). Among 22 a priori outcomes with suspected effects based on RCTs of testosterone treatment, MR analyses demonstrated that each 0.1 nmol/L increase in CFT was associated with adverse effects on increased risk of prostate cancer, risk of androgenic alopecia, and hematocrit percentage, but beneficial effects on increased heel BMD, increased body fat-free percentage and decreased body fat percentage. Findings on body composition, hematocrit, and BMD are consistent with short-term effects in randomized trials of testosterone treatment (Bhasin et al., 2018a). Although testosterone treatment has not been conclusively shown to increase risk of prostate cancer and androgenic alopecia in RCTs, androgen suppression therapies, such as of 5α-reductase inhibitors, are used as treatment for androgenic alopecia and prostate cancer (Adil and Godwin, 2017; Andriole et al., 2010). The increased risk of prostate cancer replicates effects of testosterone observed in a previous MR analysis using independent data from the PRACTICAL consortium, and further supports the role of testosterone in development of these outcomes. As the leading cause of cancer among men, the predicted 1.5-fold increased risk as a result of changes in testosterone observed after initiation of testosterone treatment warrants further investigation in clinical trials and greater scrutiny in at-risk patient populations (American Cancer Society, 2019; Bhasin et al., 2018b). Furthermore, these results cast doubt on cardiovascular, cognitive, or metabolic benefit for increased testosterone, as we do not find evidence of a beneficial effect of CFT on hard endpoints, such as dementia, MI, stroke, fractures, or T2D (Aukrust et al., 2009). Most of the estimates from MR analyses were comparable with effect sizes from RCTs (Figure 3). There was only significant heterogeneity between the effects on BMD for MR and RCT, but it is difficult to make direct comparisons due to variable change in testosterone levels after administration of testosterone in each RCT, different methods and anatomical sites of BMD estimation, and differences between short-term effects in RCTs relative to lifelong effects in MR.

Among the remaining outcomes without well-established effects from RCTs, we identified evidence of novel associations between an increased GRS for CFT with adverse effects on creatinine, HDL, apolipoprotein A, hypertension, and spinal stenosis, but beneficial effects on C-reactive protein. Higher genetically-predicted free testosterone was associated with increased creatinine (β = 0.113 SD; 95% CI = 0.079 to 0.146; p=4.78×10⁻¹¹). Mechanistically, effects of testosterone on renal function are unclear, but this effect may be mediated through the known effect of testosterone on increased muscle mass which is tightly related to serum creatinine (Carrero et al., 2009; Filler et al., 2016; Schutte et al., 1981). HDL cholesterol (β = −0.074 SD; 95% CI = −0.109 to −0.039; p=3.62×10⁻⁵) and its main protein component, apolipoprotein A (β = −0.018 g/L; 95% CI = −0.026 to −0.01; p=1.55×10⁻⁵), were both decreased with higher genetically-predicted free testosterone. Likewise, the Testosterone Trials found male participants over 65 years of age randomized to testosterone experienced mildly lowered HDL cholesterol levels after 12 months (Mohler et al., 2018; Snyder et al., 2018). Higher free testosterone was associated with decreased C-reactive protein (CRP) (β = −0.085 SD; 95% CI = −0.119 to −0.052; p=6.15×10⁻⁷). Although the Testosterone Trials did not find any change in CRP in its testosterone arm, testosterone is widely-believed to have suppressive effects on the immune system which may extend to markers of inflammation such as CRP (Trigunaite et al., 2015). Furthermore, despite no effect on SBP or DBP, our analyses suggest 0.1 mol/L higher free testosterone is associated with increased risk of hypertension (OR = 1.17; 95% CI = 1.08 to 1.27; p=1.05×10⁻⁴). Given the multifactorial nature of this disease, the apparent discrepancy between blood pressure and hypertension may be explained by an effect on other risk factors that develop into hypertension. Moreover, both human and animal studies suggest a role of testosterone on hypertension. A randomized controlled trial found testosterone administration increased levels of NT-proBNP, and studies of both transgender men and anabolic steroid users have found testosterone increased arterial stiffness and blood pressure (Bachmann et al., 2019; Hartgens and Kuipers, 2004; Velho et al., 2017). Meanwhile, animal models have shown testosterone may aggravate hypertension and exacerbate increased production of reactive oxygen species specifically in hypertensive but not normotensive rat vascular endothelial tissue (Chignalia et al., 2012; Reckelhoff et al., 1998). Testosterone is widely-believed to have anti-inflammatory and osteogenic effects, but our analyses showed an association with higher risk of spinal stenosis (OR = 2.03; 95% CI = 1.51 to 2.75; p=3.82×10⁻⁶). However, the literature shows some evidence that higher testosterone is associated with greater loss of cartilage in healthy older males, and evidence from mouse models suggest testosterone has a sex-specific role in worsening osteoarthritis, a common risk factor for spinal stenosis (Hanna, 2005; Hl et al., 2007).

In comparison to previous MR studies, our results broaden the scope of the existing literature by comprehensively assessing the effects of testosterone on 461 health outcomes including hard endpoints and intermediate biomarkers. Moreover, a key strength of this study was the stringent attempt to control for pleiotropic effects of SHBG on free testosterone by conservatively removing any genetic variants in the GRS that were associated with SHBG (p<0.05). The apparent difference between protective effects of testosterone observed in a previous MR analysis of testosterone and lack of protective effect in our study might be a result of less stringent control for pleiotropic effects of SHBG in the previous study. Given studies have identified associations between SHBG and risk of T2D independent of testosterone and a direct role of SHBG in mediating signalling on target cells, insufficient controls for SHBG may lead to residual pleiotropic effects (Lakshman et al., 2010; Rosner et al., 2010; Vikan et al., 2010). Other reasons may include genetic variants explaining less variation in testosterone levels in our study, fewer cases of T2D leading to inadequate statistical power to detect weaker effects in our study, or other differences between the populations of the UK Biobank in our study and DIAGRAM consortium used by Ruth et al., 2020.

There are several limitations of this study. First, an assumption of the MR analysis is that the effect of the genetic variant on the outcome occurs only through free testosterone levels, such that there are no pleiotropic effects through other proteins or mechanisms (Davies et al., 2018). This concern was minimized by the use of multiple genetic variants, which limited the likelihood of a common alternative pathway confounding our observation. Moreover, we performed several sensitivity analyses and excluded genetic variants associated with SHBG levels, which is a potential source of pleiotropy through its effects on other hormones. Although a stringent p-value threshold was selected for genetic variants, the winner’s curse phenomenon may still bias genetic effect sizes due to the same sample being used to select genetic variants and estimate effect sizes on testosterone. Additionally, one-sample MR may be susceptible to bias towards the confounded estimate if the genetic variants are ‘weak instruments’, which can occur if the genetic variants don’t explain enough of the variance in free testosterone levels (Davies et al., 2018). To address this concern, we confirmed the selected genetic variants were strong instruments using a common threshold in MR literature (F-statistic >10) (Davies et al., 2018). Next, the UK Biobank is generally healthier and higher socioeconomic status than the general population, so there are insufficient cases to detect effects on certain rarer outcomes, such as Alzheimer’s disease, and inadequate power to identify weaker effects of free testosterone on common outcomes. Relatedly, an inherent limitation for outcomes ascertained using linked electronic medical records is a lack of adjudication and consistent application of codes in clinical practice. In the UK Biobank, CFT levels were below the reference ranges for young healthy individuals, which may be attributable to the older age of the cohort and inherent inaccuracy of immunoassays at lower levels of total testosterone. Total testosterone levels are similarly low relative to reference ranges and comparable to previous studies in the UK Biobank (Peila et al., 2020; Petermann-Rocha et al., 2020). Additional sources of variability introduced into the total testosterone measurements include differences in fasting times, diets, and time of day at which blood was drawn from participants. Nevertheless, genetic variants associated with testosterone consistently replicated known effects of testosterone on established outcomes, such as body fat, body fat-free mass, and hematocrit (Table 1). Furthermore, although the free hormone hypothesis is still debated by experts, we found largely consistent effects on outcomes using genetically-predicted free testosterone and total testosterone (Handelsman, 2017). The only significant outcomes from MR analyses with free testosterone that showed no significant effect with total testosterone across all MR methods were HDL (p=0.55) and apolipoprotein A (p=0.45). Finally, these results represent lifelong effects of endogenous free testosterone and may not necessarily reflect effects of exogenous testosterone treatment, which can vary in duration, age of initiation, and dosage.

Taken altogether, the decision to initiate long-term testosterone use warrants careful consideration of benefits and risk. Beneficial effects on body composition, sexual function, hematocrit, and BMD should be weighed against detrimental effects on androgenic alopecia, prostate cancer, hypertension and spinal stenosis, and no detectable beneficial effects on other major clinical endpoints. Ultimately, well-designed and appropriately powered RCTs, such as the ongoing TRAVERSE trials (clinicaltrials.gov, NCT03518034), are necessary to conclusively address questions of safety and effectiveness of testosterone treatment. However, as demonstrated in this study, genetically-informed analyses can be powerful tools to aid health professionals in prioritizing allocation of limited resources towards investigating the most pressing questions.

Individual-level data cannot be provided, but it is available to all researchers by application to the UK Biobank. Summary-level GWAS data will be returned to the UK Biobank Access Team for use by other researchers. All MR results and genome-wide significant SNPs have been provided in Supplementary Tables 4 to 12 in Supplementary file 1.

1. 1. Abecasis GR
   2. Auton A
   3. Brooks LD
   4. DePristo MA
   5. Durbin RM
   6. Handsaker RE
   7. Kang HM
   8. Marth GT
   9. McVean GA
   10. 1000 Genomes Project Consortium

   (2012) An integrated map of genetic variation from 1,092 human genomes

   Nature 491:56–65.

   https://doi.org/10.1038/nature11632

   • PubMed
   • Google Scholar
2. Preprint

   1. Adams M
   2. Hill WD
   3. Howard DM
   4. Davis KAS
   5. Deary IJ
   6. Hotopf M
   7. McIntosh AM

   (2018) Factors associated with sharing email information and mental health survey participation in two large population cohorts

   bioRxiv.

   https://doi.org/10.1101/471433

   • Google Scholar
3. 1. Adil A
   2. Godwin M

   (2017) The effectiveness of treatments for androgenetic alopecia: a systematic review and meta-analysis

   Journal of the American Academy of Dermatology 77:136–141.

   https://doi.org/10.1016/j.jaad.2017.02.054

   • Google Scholar
4. Software

   1. American Cancer Society

   (2019)

   Cancer Facts & Figures 2019

   Cancer Facts & Figures 2019.
5. 1. Andriole GL
   2. Bostwick DG
   3. Brawley OW
   4. Gomella LG
   5. Marberger M
   6. Montorsi F
   7. Pettaway CA
   8. Tammela TL
   9. Teloken C
   10. Tindall DJ
   11. Somerville MC
   12. Wilson TH
   13. Fowler IL
   14. Rittmaster RS
   15. REDUCE Study Group

   (2010) Effect of dutasteride on the risk of prostate Cancer

   New England Journal of Medicine 362:1192–1202.

   https://doi.org/10.1056/NEJMoa0908127

   • PubMed
   • Google Scholar
6. 1. Aukrust P
   2. Ueland T
   3. Gullestad L
   4. Yndestad A

   (2009) Testosterone: a novel therapeutic approach in chronic heart failure?

   Journal of the American College of Cardiology 54:928–929.

   https://doi.org/10.1016/j.jacc.2009.05.039

   • PubMed
   • Google Scholar
7. 1. Bachmann KN
   2. Huang S
   3. Lee H
   4. Dichtel LE
   5. Gupta DK
   6. Burnett JC
   7. Miller KK
   8. Wang TJ
   9. Finkelstein JS

   (2019) Effect of testosterone on natriuretic Peptide Levels

   Journal of the American College of Cardiology 73:1288–1296.

   https://doi.org/10.1016/j.jacc.2018.12.062

   • Google Scholar
8. 1. Baillargeon J
   2. Kuo Y-F
   3. Westra JR
   4. Urban RJ
   5. Goodwin JS

   (2018) Testosterone prescribing in the united states, 2002-2016

   JAMA 320:200–202.

   https://doi.org/10.1001/jama.2018.7999

   • Google Scholar
9. 1. Bhasin S
   2. Brito JP
   3. Cunningham GR
   4. Hayes FJ
   5. Hodis HN
   6. Matsumoto AM
   7. Snyder PJ
   8. Swerdloff RS
   9. Wu FC
   10. Yialamas MA

   (2018a) Testosterone therapy in men with hypogonadism: an endocrine society* clinical practice guideline

   The Journal of Clinical Endocrinology & Metabolism 103:1715–1744.

   https://doi.org/10.1210/jc.2018-00229

   • Google Scholar
10. 1. Bhasin S
    2. Ellenberg SS
    3. Storer TW
    4. Basaria S
    5. Pahor M
    6. Stephens-Shields AJ
    7. Cauley JA
    8. Ensrud KE
    9. Farrar JT
    10. Cella D
    11. Matsumoto AM
    12. Cunningham GR
    13. Swerdloff RS
    14. Wang C
    15. Lewis CE
    16. Molitch ME
    17. Barrett-Connor E
    18. Crandall JP
    19. Hou X
    20. Preston P
    21. Cifelli D
    22. Snyder PJ
    23. Gill TM

    (2018b) Effect of testosterone replacement on measures of mobility in older men with mobility limitation and low testosterone concentrations: secondary analyses of the testosterone trials

    The Lancet Diabetes & Endocrinology 6:879–890.

    https://doi.org/10.1016/S2213-8587(18)30171-2

    • Google Scholar
11. 1. Bowden J
    2. Davey Smith G
    3. Burgess S

    (2015) Mendelian randomization with invalid instruments: effect estimation and Bias detection through egger regression

    International Journal of Epidemiology 44:512–525.

    https://doi.org/10.1093/ije/dyv080

    • Google Scholar
12. 1. Brion M-JA
    2. Shakhbazov K
    3. Visscher PM

    (2013) Calculating statistical power in mendelian randomization studies

    International Journal of Epidemiology 42:1497–1501.

    https://doi.org/10.1093/ije/dyt179

    • Google Scholar
13. 1. Bulik-Sullivan BK
    2. Loh P-R
    3. Finucane HK
    4. Ripke S
    5. Yang J
    6. Patterson N
    7. Daly MJ
    8. Price AL
    9. Neale BM

    (2015) LD score regression distinguishes confounding from polygenicity in genome-wide association studies

    Nature Genetics 47:291–295.

    https://doi.org/10.1038/ng.3211

    • Google Scholar
14. 1. Burgess S
    2. Dudbridge F
    3. Thompson SG

    (2016) Combining information on multiple instrumental variables in mendelian randomization: comparison of allele score and summarized data methods

    Statistics in Medicine 35:1880–1906.

    https://doi.org/10.1002/sim.6835

    • Google Scholar
15. Preprint

    1. Bycroft C
    2. Freeman C
    3. Petkova D
    4. Band G
    5. Elliott LT
    6. Sharp K
    7. Motyer A
    8. Vukcevic D
    9. Delaneau O
    10. O’Connell J
    11. Cortes A
    12. Welsh S
    13. McVean G
    14. Leslie S
    15. Donnelly P
    16. Marchini J

    (2017) Genome-wide genetic data on ~500,000 UK biobank participants

    bioRxiv.

    https://doi.org/10.1101/166298

    • Google Scholar
16. 1. Carrero JJ
    2. Qureshi AR
    3. Parini P
    4. Arver S
    5. Lindholm B
    6. Bárány P
    7. Heimbürger O
    8. Stenvinkel P

    (2009) Low serum testosterone increases mortality risk among male Dialysis patients

    Journal of the American Society of Nephrology 20:613–620.

    https://doi.org/10.1681/ASN.2008060664

    • Google Scholar
17. 1. Chignalia AZ
    2. Schuldt EZ
    3. Camargo LL
    4. Montezano AC
    5. Callera GE
    6. Laurindo FR
    7. Lopes LR
    8. Avellar MCW
    9. Carvalho MHC
    10. Fortes ZB
    11. Touyz RM
    12. Tostes RC

    (2012) Testosterone induces vascular smooth muscle cell migration by NADPH oxidase and c-Src–Dependent Pathways

    Hypertension 59:1263–1271.

    https://doi.org/10.1161/HYPERTENSIONAHA.111.180620

    • Google Scholar
18. 1. Cui Y
    2. Zong H
    3. Yan H
    4. Zhang Y

    (2014) The effect of testosterone replacement therapy on prostate Cancer: a systematic review and meta-analysis

    Prostate Cancer and Prostatic Diseases 17:132–143.

    https://doi.org/10.1038/pcan.2013.60

    • Google Scholar
19. 1. Davies NM
    2. Holmes MV
    3. Davey Smith G

    (2018) Reading mendelian randomisation studies: a guide, glossary, and checklist for clinicians

    BMJ 362:k601.

    https://doi.org/10.1136/bmj.k601

    • Google Scholar
20. 1. Denny JC
    2. Bastarache L
    3. Ritchie MD
    4. Carroll RJ
    5. Zink R
    6. Mosley JD
    7. Field JR
    8. Pulley JM
    9. Ramirez AH
    10. Bowton E
    11. Basford MA
    12. Carrell DS
    13. Peissig PL
    14. Kho AN
    15. Pacheco JA
    16. Rasmussen LV
    17. Crosslin DR
    18. Crane PK
    19. Pathak J
    20. Bielinski SJ
    21. Pendergrass SA
    22. Xu H
    23. Hindorff LA
    24. Li R
    25. Manolio TA
    26. Chute CG
    27. Chisholm RL
    28. Larson EB
    29. Jarvik GP
    30. Brilliant MH
    31. McCarty CA
    32. Kullo IJ
    33. Haines JL
    34. Crawford DC
    35. Masys DR
    36. Roden DM

    (2013) Systematic comparison of phenome-wide association study of electronic medical record data and genome-wide association study data

    Nature Biotechnology 31:1102–1111.

    https://doi.org/10.1038/nbt.2749

    • Google Scholar
21. 1. Doherty A
    2. Jackson D
    3. Hammerla N
    4. Plötz T
    5. Olivier P
    6. Granat MH
    7. White T
    8. van Hees VT
    9. Trenell MI
    10. Owen CG
    11. Preece SJ
    12. Gillions R
    13. Sheard S
    14. Peakman T
    15. Brage S
    16. Wareham NJ

    (2017) Large scale population assessment of physical activity using wrist worn accelerometers: the UK biobank study

    PLOS ONE 12:e0169649.

    https://doi.org/10.1371/journal.pone.0169649

    • Google Scholar
22. 1. Fernández-Balsells MM
    2. Murad MH
    3. Lane M
    4. Lampropulos JF
    5. Albuquerque F
    6. Mullan RJ
    7. Agrwal N
    8. Elamin MB
    9. Gallegos-Orozco JF
    10. Wang AT
    11. Erwin PJ
    12. Bhasin S
    13. Montori VM

    (2010) Adverse effects of testosterone therapy in adult men: a systematic review and Meta-Analysis

    The Journal of Clinical Endocrinology & Metabolism 95:2560–2575.

    https://doi.org/10.1210/jc.2009-2575

    • Google Scholar
23. 1. Filler G
    2. Ramsaroop A
    3. Stein R
    4. Grant C
    5. Marants R
    6. So A
    7. McIntyre C

    (2016) Is testosterone detrimental to renal function?

    Kidney International Reports 1:306–310.

    https://doi.org/10.1016/j.ekir.2016.07.004

    • Google Scholar
24. 1. Gagliano-Jucá T
    2. Basaria S

    (2019) Testosterone replacement therapy and cardiovascular risk

    Nature Reviews Cardiology 16:555–574.

    https://doi.org/10.1038/s41569-019-0211-4

    • Google Scholar
25. 1. Handelsman DJ

    (2013) Global trends in testosterone prescribing, 2000–2011: expanding the spectrum of prescription drug misuse

    Medical Journal of Australia 199:548–551.

    https://doi.org/10.5694/mja13.10111

    • Google Scholar
26. 1. Handelsman DJ

    (2017) Free testosterone: pumping up the tires or ending the free ride?

    Endocrine Reviews 38:297–301.

    https://doi.org/10.1210/er.2017-00171

    • Google Scholar
27. 1. Hanna F

    (2005) Factors influencing longitudinal change in knee cartilage volume measured from magnetic resonance imaging in healthy men

    Annals of the Rheumatic Diseases 64:1038–1042.

    https://doi.org/10.1136/ard.2004.029355

    • Google Scholar
28. 1. Hartgens F
    2. Kuipers H

    (2004) Effects of Androgenic-Anabolic steroids in Athletes

    Sports Medicine 34:513–554.

    https://doi.org/10.2165/00007256-200434080-00003

    • Google Scholar
29. 1. Hemani G
    2. Zheng J
    3. Elsworth B
    4. Wade KH
    5. Haberland V
    6. Baird D
    7. Laurin C
    8. Burgess S
    9. Bowden J
    10. Langdon R
    11. Tan VY
    12. Yarmolinsky J
    13. Shihab HA
    14. Timpson NJ
    15. Evans DM
    16. Relton C
    17. Martin RM
    18. Davey Smith G
    19. Gaunt TR
    20. Haycock PC

    (2018) The MR-Base platform supports systematic causal inference across the human phenome

    eLife 7:e34408.

    https://doi.org/10.7554/eLife.34408

    • Google Scholar
30. 1. Hl M
    2. Blanchet TJ
    3. Peluso D
    4. Hopkins B
    5. Morris EA
    6. Glasson SS

    (2007) Osteoarthritis severity is sex dependent in a surgical mouse model

    Osteoarthr Cartil 15:695–700.

    https://doi.org/10.1016/j.joca.2006.11.005

    • Google Scholar
31. 1. Holmes MV
    2. Asselbergs FW
    3. Palmer TM
    4. Drenos F
    5. Lanktree MB
    6. Nelson CP
    7. Dale CE
    8. Padmanabhan S
    9. Finan C
    10. Swerdlow DI
    11. Tragante V
    12. van Iperen EPA
    13. Sivapalaratnam S
    14. Shah S
    15. Elbers CC
    16. Shah T
    17. Engmann J
    18. Giambartolomei C
    19. White J
    20. Zabaneh D
    21. Sofat R
    22. McLachlan S
    23. Doevendans PA
    24. Balmforth AJ
    25. Hall AS
    26. North KE
    27. Almoguera B
    28. Hoogeveen RC
    29. Cushman M
    30. Fornage M
    31. Patel SR
    32. Redline S
    33. Siscovick DS
    34. Tsai MY
    35. Karczewski KJ
    36. Hofker MH
    37. Verschuren WM
    38. Bots ML
    39. van der Schouw YT
    40. Melander O
    41. Dominiczak AF
    42. Morris R
    43. Ben-Shlomo Y
    44. Price J
    45. Kumari M
    46. Baumert J
    47. Peters A
    48. Thorand B
    49. Koenig W
    50. Gaunt TR
    51. Humphries SE
    52. Clarke R
    53. Watkins H
    54. Farrall M
    55. Wilson JG
    56. Rich SS
    57. de Bakker PIW
    58. Lange LA
    59. Davey Smith G
    60. Reiner AP
    61. Talmud PJ
    62. Kivimäki M
    63. Lawlor DA
    64. Dudbridge F
    65. Samani NJ
    66. Keating BJ
    67. Hingorani AD
    68. Casas JP

    (2015) Mendelian randomization of blood lipids for coronary heart disease

    European Heart Journal 36:539–550.

    https://doi.org/10.1093/eurheartj/eht571

    • Google Scholar
32. 1. Howard DM
    2. Adams MJ
    3. Shirali M
    4. Clarke T-K
    5. Marioni RE
    6. Davies G
    7. Coleman JRI
    8. Alloza C
    9. Shen X
    10. Barbu MC
    11. Wigmore EM
    12. Gibson J
    13. Hagenaars SP
    14. Lewis CM
    15. Ward J
    16. Smith DJ
    17. Sullivan PF
    18. Haley CS
    19. Breen G
    20. Deary IJ
    21. McIntosh AM

    (2018) Genome-wide association study of depression phenotypes in UK biobank identifies variants in excitatory synaptic pathways

    Nature Communications 9:1470.

    https://doi.org/10.1038/s41467-018-03819-3

    • Google Scholar
33. 1. Jasuja GK
    2. Bhasin S
    3. Rose AJ

    (2017) Patterns of testosterone prescription overuse

    Current Opinion in Endocrinology & Diabetes and Obesity 24:240–245.

    https://doi.org/10.1097/MED.0000000000000336

    • Google Scholar
34. 1. Lakshman KM
    2. Bhasin S
    3. Araujo AB

    (2010) Sex Hormone-Binding globulin as an independent predictor of incident type 2 diabetes mellitus in men

    The Journals of Gerontology Series A: Biological Sciences and Medical Sciences 65A:503–509.

    https://doi.org/10.1093/gerona/glq002

    • Google Scholar
35. 1. Layton JB
    2. Li D
    3. Meier CR
    4. Sharpless JL
    5. Stürmer T
    6. Jick SS
    7. Brookhart MA

    (2014) Testosterone lab testing and initiation in the united kingdom and the united states, 2000 to 2011

    The Journal of Clinical Endocrinology & Metabolism 99:835–842.

    https://doi.org/10.1210/jc.2013-3570

    • Google Scholar
36. 1. Loh P-R
    2. Tucker G
    3. Bulik-Sullivan BK
    4. Vilhjálmsson BJ
    5. Finucane HK
    6. Salem RM
    7. Chasman DI
    8. Ridker PM
    9. Neale BM
    10. Berger B
    11. Patterson N
    12. Price AL

    (2015) Efficient bayesian mixed-model analysis increases association power in large cohorts

    Nature Genetics 47:284–290.

    https://doi.org/10.1038/ng.3190

    • Google Scholar
37. 1. McCarthy S
    2. Das S
    3. Kretzschmar W
    4. Delaneau O
    5. Wood AR
    6. Teumer A
    7. Kang HM
    8. Fuchsberger C
    9. Danecek P
    10. Sharp K
    11. Luo Y
    12. Sidore C
    13. Kwong A
    14. Timpson N
    15. Koskinen S
    16. Vrieze S
    17. Scott LJ
    18. Zhang H
    19. Mahajan A
    20. Veldink J
    21. Peters U
    22. Pato C
    23. van Duijn CM
    24. Gillies CE
    25. Gandin I
    26. Mezzavilla M
    27. Gilly A
    28. Cocca M
    29. Traglia M
    30. Angius A
    31. Barrett JC
    32. Boomsma D
    33. Branham K
    34. Breen G
    35. Brummett CM
    36. Busonero F
    37. Campbell H
    38. Chan A
    39. Chen S
    40. Chew E
    41. Collins FS
    42. Corbin LJ
    43. Smith GD
    44. Dedoussis G
    45. Dorr M
    46. Farmaki AE
    47. Ferrucci L
    48. Forer L
    49. Fraser RM
    50. Gabriel S
    51. Levy S
    52. Groop L
    53. Harrison T
    54. Hattersley A
    55. Holmen OL
    56. Hveem K
    57. Kretzler M
    58. Lee JC
    59. McGue M
    60. Meitinger T
    61. Melzer D
    62. Min JL
    63. Mohlke KL
    64. Vincent JB
    65. Nauck M
    66. Nickerson D
    67. Palotie A
    68. Pato M
    69. Pirastu N
    70. McInnis M
    71. Richards JB
    72. Sala C
    73. Salomaa V
    74. Schlessinger D
    75. Schoenherr S
    76. Slagboom PE
    77. Small K
    78. Spector T
    79. Stambolian D
    80. Tuke M
    81. Tuomilehto J
    82. Van den Berg LH
    83. Van Rheenen W
    84. Volker U
    85. Wijmenga C
    86. Toniolo D
    87. Zeggini E
    88. Gasparini P
    89. Sampson MG
    90. Wilson JF
    91. Frayling T
    92. de Bakker PI
    93. Swertz MA
    94. McCarroll S
    95. Kooperberg C
    96. Dekker A
    97. Altshuler D
    98. Willer C
    99. Iacono W
    100. Ripatti S
    101. Soranzo N
    102. Walter K
    103. Swaroop A
    104. Cucca F
    105. Anderson CA
    106. Myers RM
    107. Boehnke M
    108. McCarthy MI
    109. Durbin R
    110. Haplotype Reference Consortium

    (2016) A reference panel of 64,976 haplotypes for genotype imputation

    Nature Genetics 48:1279–1283.

    https://doi.org/10.1038/ng.3643

    • PubMed
    • Google Scholar
38. 1. Mohler ER
    2. Ellenberg SS
    3. Lewis CE
    4. Wenger NK
    5. Budoff MJ
    6. Lewis MR
    7. Barrett-Connor E
    8. Swerdloff RS
    9. Stephens-Shields A
    10. Bhasin S
    11. Cauley JA
    12. Crandall JP
    13. Cunningham GR
    14. Ensrud KE
    15. Gill TM
    16. Matsumoto AM
    17. Molitch ME
    18. Pahor M
    19. Preston PE
    20. Hou X
    21. Cifelli D
    22. Snyder PJ

    (2018) The effect of testosterone on cardiovascular biomarkers in the testosterone trials

    The Journal of Clinical Endocrinology & Metabolism 103:681–688.

    https://doi.org/10.1210/jc.2017-02243

    • PubMed
    • Google Scholar
39. 1. Ng Tang Fui M
    2. Prendergast LA
    3. Dupuis P
    4. Raval M
    5. Strauss BJ
    6. Zajac JD
    7. Grossmann M

    (2016) Effects of testosterone treatment on body fat and lean mass in obese men on a hypocaloric diet: a randomised controlled trial

    BMC Medicine 14:153.

    https://doi.org/10.1186/s12916-016-0700-9

    • PubMed
    • Google Scholar
40. 1. Ohlsson C
    2. Wallaschofski H
    3. Lunetta KL
    4. Stolk L
    5. Perry JR
    6. Koster A
    7. Petersen AK
    8. Eriksson J
    9. Lehtimäki T
    10. Huhtaniemi IT
    11. Hammond GL
    12. Maggio M
    13. Coviello AD
    14. Ferrucci L
    15. Heier M
    16. Hofman A
    17. Holliday KL
    18. Jansson JO
    19. Kähönen M
    20. Karasik D
    21. Karlsson MK
    22. Kiel DP
    23. Liu Y
    24. Ljunggren O
    25. Lorentzon M
    26. Lyytikäinen LP
    27. Meitinger T
    28. Mellström D
    29. Melzer D
    30. Miljkovic I
    31. Nauck M
    32. Nilsson M
    33. Penninx B
    34. Pye SR
    35. Vasan RS
    36. Reincke M
    37. Rivadeneira F
    38. Tajar A
    39. Teumer A
    40. Uitterlinden AG
    41. Ulloor J
    42. Viikari J
    43. Völker U
    44. Völzke H
    45. Wichmann HE
    46. Wu TS
    47. Zhuang WV
    48. Ziv E
    49. Wu FC
    50. Raitakari O
    51. Eriksson A
    52. Bidlingmaier M
    53. Harris TB
    54. Murray A
    55. de Jong FH
    56. Murabito JM
    57. Bhasin S
    58. Vandenput L
    59. Haring R
    60. EMAS Study Group

    (2011) Genetic determinants of serum testosterone concentrations in men

    PLOS Genetics 7:e1002313.

    https://doi.org/10.1371/journal.pgen.1002313

    • PubMed
    • Google Scholar
41. 1. Peila R
    2. Arthur RS
    3. Rohan TE

    (2020) Association of sex hormones with risk of cancers of the pancreas, kidney, and brain in the UK biobank cohort study

    Cancer Epidemiology Biomarkers & Prevention 29:1832–1836.

    https://doi.org/10.1158/1055-9965.EPI-20-0246

    • Google Scholar
42. 1. Petermann-Rocha F
    2. Gray SR
    3. Pell JP
    4. Celis-Morales C
    5. Ho FK

    (2020) Biomarkers profile of people with Sarcopenia: a Cross-sectional analysis from UK biobank

    Journal of the American Medical Directors Association 5:e005.

    https://doi.org/10.1016/j.jamda.2020.05.005

    • Google Scholar
43. 1. Pierce BL
    2. Ahsan H
    3. Vanderweele TJ

    (2011) Power and instrument strength requirements for mendelian randomization studies using multiple genetic variants

    International Journal of Epidemiology 40:740–752.

    https://doi.org/10.1093/ije/dyq151

    • PubMed
    • Google Scholar
44. 1. Reckelhoff JF
    2. Zhang H
    3. Granger JP

    (1998) Testosterone exacerbates hypertension and reduces pressure-natriuresis in male spontaneously hypertensive rats

    Hypertension 31:435–439.

    https://doi.org/10.1161/01.HYP.31.1.435

    • PubMed
    • Google Scholar
45. 1. Rosner W
    2. Hryb DJ
    3. Kahn SM
    4. Nakhla AM
    5. Romas NA

    (2010) Interactions of sex hormone-binding globulin with target cells

    Molecular and Cellular Endocrinology 316:79–85.

    https://doi.org/10.1016/j.mce.2009.08.009

    • PubMed
    • Google Scholar
46. 1. Ross S
    2. Gerstein HC
    3. Eikelboom J
    4. Anand SS
    5. Yusuf S
    6. Paré G

    (2015) Mendelian randomization analysis supports the causal role of dysglycaemia and diabetes in the risk of coronary artery disease

    European Heart Journal 36:1454–1462.

    https://doi.org/10.1093/eurheartj/ehv083

    • PubMed
    • Google Scholar
47. 1. Ruth KS
    2. Day FR
    3. Tyrrell J
    4. Thompson DJ
    5. Wood AR
    6. Mahajan A
    7. Beaumont RN
    8. Wittemans L
    9. Martin S
    10. Busch AS
    11. Erzurumluoglu AM
    12. Hollis B
    13. O'Mara TA
    14. McCarthy MI
    15. Langenberg C
    16. Easton DF
    17. Wareham NJ
    18. Burgess S
    19. Murray A
    20. Ong KK
    21. Frayling TM
    22. Perry JRB
    23. Endometrial Cancer Association Consortium

    (2020) Using human genetics to understand the disease impacts of testosterone in men and women

    Nature Medicine 26:252–258.

    https://doi.org/10.1038/s41591-020-0751-5

    • PubMed
    • Google Scholar
48. 1. Schooling CM
    2. Luo S
    3. Au Yeung SL
    4. Thompson DJ
    5. Karthikeyan S
    6. Bolton TR
    7. Mason AM
    8. Ingelsson E
    9. Burgess S

    (2018) Genetic predictors of testosterone and their associations with cardiovascular disease and risk factors: a mendelian randomization investigation

    International Journal of Cardiology 267:171–176.

    https://doi.org/10.1016/j.ijcard.2018.05.051

    • PubMed
    • Google Scholar
49. 1. Schutte JE
    2. Longhurst JC
    3. Gaffney FA
    4. Bastian BC
    5. Blomqvist CG

    (1981) Total plasma creatinine: an accurate measure of total striated muscle mass

    Journal of Applied Physiology 51:762–766.

    https://doi.org/10.1152/jappl.1981.51.3.762

    • PubMed
    • Google Scholar
50. 1. Snyder PJ
    2. Bhasin S
    3. Cunningham GR
    4. Matsumoto AM
    5. Stephens-Shields AJ
    6. Cauley JA
    7. Gill TM
    8. Barrett-Connor E
    9. Swerdloff RS
    10. Wang C
    11. Ensrud KE
    12. Lewis CE
    13. Farrar JT
    14. Cella D
    15. Rosen RC
    16. Pahor M
    17. Crandall JP
    18. Molitch ME
    19. Resnick SM
    20. Budoff M
    21. Mohler ER
    22. Wenger NK
    23. Cohen HJ
    24. Schrier S
    25. Keaveny TM
    26. Kopperdahl D
    27. Lee D
    28. Cifelli D
    29. Ellenberg SS

    (2018) Lessons from the testosterone trials

    Endocrine Reviews 39:369–386.

    https://doi.org/10.1210/er.2017-00234

    • PubMed
    • Google Scholar
51. 1. Sudlow C
    2. Gallacher J
    3. Allen N
    4. Beral V
    5. Burton P
    6. Danesh J
    7. Downey P
    8. Elliott P
    9. Green J
    10. Landray M
    11. Liu B
    12. Matthews P
    13. Ong G
    14. Pell J
    15. Silman A
    16. Young A
    17. Sprosen T
    18. Peakman T
    19. Collins R

    (2015) UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age

    PLOS Medicine 12:e1001779.

    https://doi.org/10.1371/journal.pmed.1001779

    • PubMed
    • Google Scholar
52. 1. Traustadóttir T
    2. Harman SM
    3. Tsitouras P
    4. Pencina KM
    5. Li Z
    6. Travison TG
    7. Eder R
    8. Miciek R
    9. McKinnon J
    10. Woodbury E
    11. Basaria S
    12. Bhasin S
    13. Storer TW

    (2018) Long-Term testosterone supplementation in older men attenuates Age-Related decline in aerobic capacity

    The Journal of Clinical Endocrinology & Metabolism 103:2861–2869.

    https://doi.org/10.1210/jc.2017-01902

    • PubMed
    • Google Scholar
53. 1. Trigunaite A
    2. Dimo J
    3. Jørgensen TN

    (2015) Suppressive effects of androgens on the immune system

    Cellular Immunology 294:87–94.

    https://doi.org/10.1016/j.cellimm.2015.02.004

    • PubMed
    • Google Scholar
54. 1. Velho I
    2. Fighera TM
    3. Ziegelmann PK
    4. Spritzer PM

    (2017) Effects of testosterone therapy on BMI, blood pressure, and laboratory profile of transgender men: a systematic review

    Andrology 5:881–888.

    https://doi.org/10.1111/andr.12382

    • PubMed
    • Google Scholar
55. 1. Verbanck M
    2. Chen CY
    3. Neale B
    4. Do R

    (2018) Detection of widespread horizontal pleiotropy in causal relationships inferred from mendelian randomization between complex traits and diseases

    Nature Genetics 50:693–698.

    https://doi.org/10.1038/s41588-018-0099-7

    • PubMed
    • Google Scholar
56. 1. Vermeulen A
    2. Verdonck L
    3. Kaufman JM

    (1999) A critical evaluation of simple methods for the estimation of free testosterone in serum

    The Journal of Clinical Endocrinology & Metabolism 84:3666–3672.

    https://doi.org/10.1210/jcem.84.10.6079

    • PubMed
    • Google Scholar
57. 1. Vikan T
    2. Schirmer H
    3. Njølstad I
    4. Svartberg J

    (2010) Low testosterone and sex hormone-binding globulin levels and high estradiol levels are independent predictors of type 2 diabetes in men

    European Journal of Endocrinology 162:747–754.

    https://doi.org/10.1530/EJE-09-0943

    • PubMed
    • Google Scholar
58. 1. Wu P
    2. Gifford A
    3. Meng X
    4. Li X
    5. Campbell H
    6. Varley T
    7. Zhao J
    8. Carroll R
    9. Bastarache L
    10. Denny JC
    11. Theodoratou E
    12. Wei W-Q

    (2019) Mapping ICD-10 and ICD-10-CM codes to phecodes: workflow development and initial evaluation

    JMIR Medical Informatics 7:e14325.

    https://doi.org/10.2196/14325

    • Google Scholar
59. 1. Zhang Z
    2. Kang D
    3. Li H

    (2020) The effects of testosterone on bone health in males with testosterone deficiency: a systematic review and meta-analysis

    BMC Endocrine Disorders 20:1–12.

    https://doi.org/10.1186/s12902-020-0509-6

    • Google Scholar
60. 1. Zhao J
    2. Jiang C
    3. Lam TH
    4. Liu B
    5. Cheng KK
    6. Xu L
    7. Au Yeung SL
    8. Zhang W
    9. Leung GM
    10. Schooling CM

    (2014) Genetically predicted testosterone and cardiovascular risk factors in men: a mendelian randomization analysis in the guangzhou biobank cohort study

    International Journal of Epidemiology 43:140–148.

    https://doi.org/10.1093/ije/dyt239

    • PubMed
    • Google Scholar
61. Preprint

    1. Zhao Q
    2. Wang J
    3. Hemani G
    4. Bowden J
    5. Small DS

    (2018) Statistical inference in two-sample summary-data mendelian randomization using robust adjusted profile score

    arXiv.

    https://arxiv.org/abs/1801.09652

    • Google Scholar

Author details

1. Pedrum Mohammadi-Shemirani

   1. Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton, Canada
   2. Thrombosis and Atherosclerosis Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton, Canada
   3. Department of Medical Sciences, McMaster University, Hamilton, Canada

   Contribution

   Data curation, Software, Formal analysis, Investigation, Visualization, Writing - original draft

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-6740-7858

2. Michael Chong

   1. Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton, Canada
   2. Thrombosis and Atherosclerosis Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton, Canada
   3. Department of Biochemistry and Biomedical Sciences, McMaster University, Hamilton, Canada

   Contribution

   Data curation, Software, Formal analysis, Writing - review and editing

   Competing interests

   No competing interests declared

3. Marie Pigeyre

   1. Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton, Canada
   2. Department of Medicine, McMaster University, Hamilton Health Sciences, Hamilton, Canada

   Contribution

   Conceptualization, Writing - review and editing, Analysis and interpretation of data

   Competing interests

   No competing interests declared

4. Robert W Morton

   Department of Kinesiology, McMaster University, Hamilton, Canada

   Contribution

   Writing - review and editing, Analysis and interpretation of data

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-0099-4167

5. Hertzel C Gerstein

   1. Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton, Canada
   2. Department of Medicine, McMaster University, Hamilton Health Sciences, Hamilton, Canada

   Contribution

   Conceptualization, Writing - review and editing, Analysis and interpretation of data

   Competing interests

   HCG reports research grants from Eli Lilly, AstraZeneca, Merck, Novo Nordisk, and Sanofi; honoraria for speaking from AstraZeneca, Boehringer Ingelheim, Eli Lilly, Novo Nordisk, and Sanofi; and consulting fees from Abbott, AstraZeneca, Boehringer Ingelheim, Eli Lilly, Merck, Novo Nordisk, Janssen, Sanofi, Kowa, and Cirius.

6. Guillaume Paré

   1. Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton, Canada
   2. Thrombosis and Atherosclerosis Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton, Canada
   3. Department of Pathology and Molecular Medicine, McMaster University, Michael G. DeGroote School of Medicine, Hamilton, Canada
   4. Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Canada

   Contribution

   Conceptualization, Data curation, Supervision, Funding acquisition, Methodology, Project administration, Writing - review and editing

   For correspondence

   pareg@mcmaster.ca

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-6795-4760

• 8,766

  views

• 335

  downloads

• 36

  citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.