The association between testosterone, estradiol, estrogen sulfotransferase and idiopathic pulmonary fibrosis: a bidirectional Mendelian randomization study

The association between testosterone, estradiol, estrogen sulfotransferase and idiopathic pulmonary fibrosis: a bidirectional Mendelian randomization study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The association between testosterone, estradiol, estrogen sulfotransferase and idiopathic pulmonary fibrosis: a bidirectional Mendelian randomization study Qingying Xu, Guangwang Hu, Qunying Lin, Menghang Wu, Kenan Tang, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3928046/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 03 Sep, 2024 Read the published version in BMC Pulmonary Medicine → Version 1 posted 12 You are reading this latest preprint version Abstract Background The causal relationships between testosterone, estradiol, estrogen sulfotransferase, and idiopathic pulmonary fibrosis (IPF) are not well understood. This study employs a bidirectional two-sample Mendelian Randomization (MR) approach to explore these associations. Methods We extracted significant genetic loci associated with testosterone, estradiol, and estrogen sulfotransferase from GWAS summary data as instrumental variables, with IPF as the outcome variable for a two-sample Mendelian randomization analysis. Instrumental variables and outcome variables were then swapped for a bidirectional two-sample Mendelian randomization analysis. The inverse variance weighted (IVW), MR-Egger, and weighted median methods were used to evaluate causal relationships. Cochran's Q test, MR-Egger regression, MR-PRESSO global test, and leave-one-out method were used for sensitivity analyses. Results Genetically predicted increases in serum testosterone levels by one standard deviation were associated with a 58.7% decrease in the risk of developing IPF (OR = 0.413, P IVW =0.029, 95% CI = 0.187 ~ 0.912), while an increase in serum estrogen sulfotransferase by one standard deviation was associated with a 32.4% increase in risk (OR = 1.324, P IVW =0.006, 95% CI = 1.083 ~ 1.618). No causal relationship was found between estradiol (OR = 1.094, P IVW =0.735, 95% CI = 0.650 ~ 1.841) and the risk of IPF. Reverse MR analysis did not reveal any causal relationship between IPF and testosterone (OR = 1.001, P IVW =0.51, 95% CI = 0.998 ~ 1.004), estradiol (OR = 1.001, P IVW =0.958, 95% CI = 0.982 ~ 1.019), or estrogen sulfotransferase (OR = 0.975, P IVW =0.251, 95% CI = 0.933 ~ 1.018). Conclusions Increased serum levels of testosterone are associated with a reduced risk of IPF, while increased levels of serum estrogen sulfotransferase are associated with an increased risk. No causal relationship was found between estradiol and the development of IPF. No causal relationship was identified between IPF and testosterone, estradiol, or estrogen sulfotransferase. Idiopathic pulmonary fibrosis Sex hormones Testosterone Estrogen sulfotransferase Mendelian randomization Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Idiopathic pulmonary fibrosis (IPF) is a chronic interstitial lung disease characterized by progressive and irreversible pulmonary fibrosis. The etiology and pathogenesis of IPF are complex and not yet fully understood. Risk factors for IPF include aging, smoking history, genetic factors, environmental exposure, lung microbiota, gastroesophageal reflux, and more[1]. It is currently believed that IPF is caused by persistent or repeated lung epithelial injury and subsequent activation of fibroblasts and myofibroblast differentiation[2]. Persistent myofibroblast expression leads to excessive extracellular matrix deposition, abnormal lung repair, tissue scarring, distortion of alveolar structure, and irreversible loss of lung function[2]. According to a study covering 12 countries, the adjusted incidence and prevalence rates of IPF are 0.09–1.2/10,000 and 0.33–4.51/10,000, respectively[3]. IPF predominantly affects males, with a French multicenter prospective study reporting that 78% of 236 newly diagnosed IPF patients were male, and 22% were female[4]. IPF progresses rapidly and has a poor prognosis. A systematic review and meta-analysis of 63,307 patients from 20 countries showed 3-year and 5-year survival rates of 61.8% (95% CI 58.7 ~ 64.9) and 45.6% (95% CI 41.5 ~ 49.7), respectively[5]. Currently, FDA-approved drugs for treating IPF include Pirfenidone and Nintedanib, both of which can slow the progression of IPF but cannot reverse established pulmonary fibrosis and are associated with tolerability issues[6, 7]. Lung transplantation remains the best option for treating IPF, but it is plagued by issues such as donor shortage, surgical risks, infection risk, and rejection reactions. Therefore, exploring new treatment approaches for IPF is imperative. Sex hormones are a class of biological molecules produced by the endocrine system, primarily including androgens and estrogens. Testosterone and estradiol are the most prominent biologically active forms of androgens and estrogens, respectively[8, 9]. Sex hormones play important roles not only in sexual development and reproductive system function but also in influencing the immune system and metabolism. Estrogen sulfotransferase is the enzyme with the highest affinity for estrogens and is primarily responsible for catalyzing the sulfation of estrogens[10, 11]. Estrogen sulfotransferase has also been found to be involved in the sulfation of dehydroepiandrosterone and thyroid hormones[10]. Sex hormones have been found to participate in the fibrotic processes in multiple organs, including the heart, kidneys, liver, and more[12–16]. However, research on the role of sex hormones in IPF is limited, and some results are contradictory. A case-control study involving 101 male IPF patients and 51 healthy controls found a significant decrease in testosterone levels in the IPF group[17]. Furthermore, Hideki Nawa et al. found that anti-androgen drugs might lead to interstitial pneumonia, possibly by inhibiting the binding of androgens to receptors[18]. Regarding estradiol, L. Cody Smith and colleagues discovered that estradiol may reduce lung fibrosis occurrence by specifically downregulating the expression of CLIC 3 and RBP 7, genes associated with IPF, without affecting the TGF-β1 signaling pathway[19]. Research by Yonghong Xiao and others showed that estradiol could inhibit the development of lung fibrosis in mice by upregulating the expression of Caveolin-1 and suppressing the expression of type III collagen[20]. In addition, Solopov P. and colleagues' study indicated that dietary phytoestrogen intake could alleviate lung fibrosis in mice[21]. However, Mehrnaz Gharaee-Kermani and colleagues arrived at different conclusions, finding that female rats had more severe lung fibrotic responses than male rats, ovariectomy reduced lung fibrosis severity, and estradiol replacement therapy restored fibrotic responses, suggesting a potential pro-fibrotic role of estrogens[22]. Stevan P Tofovic and colleagues' study demonstrated that estrogen had an anti-mitotic effect on human lung fibroblasts only at high pharmacological concentrations (5µM) and had no effect on the growth of human lung fibroblasts at a concentration of 10µM[23]. In contrast, 2-methoxyestradiol (an endogenous metabolite of estradiol) inhibited the growth of human lung fibroblasts in a concentration-dependent manner[23]. Currently, research on the role of estrogen sulfotransferase in IPF is still lacking. Therefore, further studies are needed to explore the causal relationships between testosterone, estradiol, estrogen sulfotransferase, and IPF. Mendelian Randomization (MR) analysis is a research method in genetic epidemiology proposed by Professor Katan. The basic principle of MR is to use genetic variation as instrumental variables to infer causality between exposure and outcome [24]. Unlike traditional studies that can only discover associations between variables, MR can provide stronger evidence for causal inference. Furthermore, MR's greatest advantage is that genetic variation is randomly allocated at conception, reducing the possibility of reverse causation and the influence of confounding factors[25]. Compared to observational studies, which are susceptible to bias and confounding factors, and experimental studies, which are costly and subject to ethical limitations and participant compliance issues, MR offers a robust approach for causal inference. MR must satisfy three core assumptions[24]: the relevance assumption, where instrumental variables must be closely related to the exposure; the independence assumption, where instrumental variables are not influenced by potential confounding factors; and the exclusion restriction assumption, where genetic variation affects the outcome only through the exposure (Figure.1) . Figure 1 Three assumptions for IVs in MR analysis. In this study, our data were derived from a genome-wide association study (GWAS), and we employed a two-sample bidirectional MR approach to investigate the causal relationships between testosterone, estradiol, estrogen sulfotransferase, and IPF. Methods Study Design We conducted a two-sample bidirectional MR study to explore the causal relationships between testosterone, estradiol, estrogen sulfotransferase and IPF. Causal effects were assessed using the IVW method, MR-Egger method, and weighted median method (WME). We also performed Cochran's Q test to assess heterogeneity and used MR-Egger regression and MR-PRESO method to examine pleiotropy. Additionally, we conducted a leave-one-out analysis to assess the robustness of our findings. Data Sources In this study, genetic data for testosterone, estradiol, estrogen sulfotransferase, and IPF were obtained from the IEU GWAS database ( https://gwas.mrcieu.ac.uk/ ). All data used in this study are in the public domain and do not require additional ethical approval. Study populations were of European ancestry to minimize potential bias due to racial factors. The dataset for testosterone (GWAS ID: ebi-a-GCST90014013) included 353,805 samples and 10,783,644 single nucleotide polymorphisms (SNPs). The dataset for estradiol (GWAS ID: ebi-a-GCST90020092) consisted of 206,927 samples and 16,136,413 SNPs. The dataset for estrogen sulfotransferase (GWAS ID: prot-a-2892) included 3,301 samples and 10,534,735 SNPs. The dataset for IPF (GWAS ID: finn-b-IPF) comprised 198,014 samples and 16,380,413 SNPs (Table.1). Table.1 Information of the exposures and outcome datasets. Phenotype IEU GWAS id Sample size Numbers of SNPs Testosterone ebi-a-GCST90014013 353,805 10,783,644 Estradiol ebi-a- GCST90020092 163,985 7,488,193 Estrogen sulfotransferase prot-a-2892 3,301 10,534,735 IPF finn-b-IPF 198,014 16,380,413 SNP single nucleotide polymorphism. Instrumental Variables To ensure a strong correlation between genetic variation and exposure, we extracted instrumental variables for testosterone, estrogen sulfotransferase, and IPF with a threshold of P < 5×10 − 8 and for estradiol with a threshold of P < 5×10 − 7 (only 2 SNPs were obtained with P < 5×10 − 8 for estradiol). Furthermore, to mitigate linkage disequilibrium (LD), we set clustering thresholds at r 2 10,000 kb. We then excluded SNPs with an F-statistic less than 10, calculated using the formula F = R 2 (n-k-1)/k(1-R 2 ), to avoid weak instrumental variables. Additionally, we ensured data harmonization by removing palindromic SNPs. Lastly, we performed a global outlier test using MR-PRESO and removed any outliers. MR Analysis In this study, we used R software version 4.3.2 and conducted the analysis using the TwoSampleMR package and MR-PRESO package. Causal effects were assessed using the IVW method, MR-Egger method, and WME method. The IVW method estimates the final causal effect by calculating the weighted average of the effect sizes and standard errors of each genetic variant, thereby reducing the impact of genetic variants with larger measurement errors[26]. The MR-Egger method accounts for the presence of an intercept and can be used to assess pleiotropy[27]. The WME method assumes that over half of the instrumental variables are valid, weights each instrumental variable's effect by its precision, and then calculates the median[28]. IVW is a robust method that fully utilizes all instrumental variables and offers higher statistical power compared to other methods. Therefore, in this study, we adopted IVW as the primary method for evaluating causal effects, with the other methods used for result validation. Sensitivity Analysis We conducted Cochran's Q test to assess heterogeneity, used MR-Egger regression and MR-PRESO method to examine pleiotropy, and performed a leave-one-out analysis to assess robustness. A Cochran Q test with P > 0.05 indicates no heterogeneity. If the MR Egger intercept has a P > 0.05, it suggests no horizontal pleiotropy. If the MR-PRESO Global test has P < 0.05, it indicates the presence of horizontal pleiotropy. MR-PRESO also detects outlier SNPs, which we removed before re-conducting MR analysis. The leave-one-out analysis involves systematically removing each SNP and computing the remaining results to assess the impact of the excluded SNP on causal effects. Results MR Results and Sensitivity Analysis for Testosterone, Estradiol, Estrogen Sulfotransferase and IPF Genetically predicted serum testosterone levels were associated with a 58.7% reduced risk of IPF for everyone standard deviation increase (OR = 0.413, P IVW =0.029, 95% CI = 0.187 ~ 0.912), while genetically predicted estrogen sulfotransferase levels were associated with a 32.4% increased risk of IPF for everyone standard deviation increase (OR = 1.324, P IVW =0.006, 95% CI = 1.083 ~ 1.618). Leave-one-out analysis for estradiol revealed that rs2345568 had a significant impact on the results and was removed. No causal relationship was found between estradiol and IPF (OR = 1.094, P IVW =0.735, 95% CI = 0.650 ~ 1.841). Cochran's Q test for IVW on testosterone, estradiol, and estrogen sulfotransferase had P-values of 0.24, 0.88, and 0.646, respectively, all greater than 0.05, indicating no significant heterogeneity. MR-Egger intercepts were 0.178, 0.83, and 0.87 for testosterone, estradiol, and MR-PRESO Global test P-values were 0.262 and 0.871 for testosterone and estradiol, respectively, all greater than 0.05. MR-Egger and MR-PRESO methods suggested no evidence of potential horizontal pleiotropy. The Figs. 2 shows the results. Leave-one-out analysis showed no statistically significant differences in the effect estimates for each SNP( Figs. 3 ). The scatter plots depict the estimated impact of IVs on exposure and outcomes (Supplementary Fig. 1). Forest plots, Funnel plots and Density plot can be found in supplementary Figs. 2–4. Figure 2 Forest plot showing results and sensibility analysis from MR study. Figure 3 The results of leave-one-out analysis for Testosterone, Estradiol, Estrogen Sulfotransferase and IPF in turn. MR Results and Sensitivity Analysis for IPF in relation to Testosterone, Estradiol, and Estrogen Sulfotransferase No causal relationships were observed between IPF and testosterone (OR = 1.001, P IVW =0.51, 95% CI = 0.998 ~ 1.004), estradiol (OR = 1.001, P IVW =0.958, 95% CI = 0.982 ~ 1.019), or estrogen sulfotransferase (OR = 0.975, P IVW =0.251, 95% CI = 0.933 ~ 1.018). Cochran's Q test for IVW on IPF with estradiol and estrogen sulfotransferase had P-values of 0.53 and 0.29, respectively, both greater than 0.05, indicating no significant heterogeneity. However, for IPF and testosterone, the IVW method had a Cochran's Q test P-value of 0.04, suggesting the presence of heterogeneity. MR-Egger intercepts for IPF and testosterone, estradiol, and estrogen sulfotransferase were 0.78, 0.88, and 0.9, respectively, all greater than 0.05. MR-PRESO Global test P-values for IPF and testosterone, estradiol, and estrogen sulfotransferase were 0.169, 0.673, and 0.419, respectively. MR-Egger and MR-PRESO methods indicated no evidence of potential horizontal pleiotropy. The Figs. 4 shows the results. Leave-one-out analysis showed no statistically significant differences in the effect estimates for each SNP ( Figs. 5 ). The scatter plots depict the estimated impact of IVs on exposure and outcomes (Supplementary Fig. 5). Forest plots, Funnel plots and Density plot can be found in supplementary Figs. 6–8. Figure 4 Forest plot showing results and sensibility analysis from reverse MR study. Figure 5 The results of leave-one-out analysis for IPF in relation to Testosterone, Estradiol, and Estrogen Sulfotransferase in turn. Discussion Our study employed a two-sample bidirectional Mendelian randomization (MR) approach to investigate the associations between testosterone, estradiol, estrogen sulfotransferase levels, and the risk of IPF (IPF). We observed that higher serum testosterone levels were associated with a decreased risk of IPF, while an increase in serum estrogen sulfotransferase levels may potentially elevate the risk of IPF. However, no causal relationship was found between estradiol levels and the occurrence of IPF. In the reverse MR analysis, we did not find any causal relationships between IPF and testosterone, estradiol, or estrogen sulfotransferase. Prior studies have suggested a potential role for sex hormones in the development of IPF, but findings have been inconsistent, and the exact mechanisms are still debated. Our MR results contribute new evidence to this ongoing debate. Our findings may shed light on the protective role of testosterone against IPF. Previous studies have reported significantly reduced testosterone levels in IPF patients, with a positive correlation between testosterone levels and telomere length, a common susceptibility factor in sporadic and familial IPF[17, 29]. Telomere length has been inversely associated with IPF development in previous Mendelian randomization studies[30], suggesting that modulation of telomere length could be one of the mechanisms through which testosterone influences IPF. IPF predominantly affects elderly males, and the decline in testosterone levels with aging may be associated with this trend. TGF-β plays a crucial role in IPF pathogenesis by stimulating fibroblast activation and proliferation and is one of the targets of the anti-fibrotic drug Pirfenidone [31, 32]. Jia et al. demonstrated that testosterone propionate (an exogenous androgen) could improve renal fibrosis in aged rats by inhibiting the TGF-β1/Smad pathway and activating the Nrf2-ARE signaling pathway[16]. The renin-angiotensin system (RAS) is also involved in the pathogenesis of IPF, with ACE-AngⅡ-AT1R promoting tissue fibrosis and ACE2-Ang(1–7)-AT2R antagonizing fibrosis progression[33]. Despite increased AT2R expression in IPF patients, the AT1R pro-fibrotic axis still predominates. Yang et al. found that testosterone could inhibit Ang II-induced excessive proliferation and collagen synthesis in cardiac fibroblasts by suppressing the ERK1/2 pathway[15]. From these studies, we can infer that the protective role of testosterone in IPF may be associated with the inhibition of the TGF-β and Ang II pathways. Testosterone also possesses immunomodulatory properties, suppressing inflammation by increasing anti-inflammatory cytokine IL-10 and reducing pro-inflammatory cytokines TNFα, IL-1β, and IL-6[34]. IL-6 not only participates in IPF-associated inflammatory responses but also promotes fibroblast proliferation[35]. Furthermore, studies have shown that testosterone can improve mitochondrial function in heart, muscle, and brain tissues[36–38], while mitochondrial dysfunction plays a significant role in IPF pathogenesis[39]. Criselda Mendoza-Milla et al. found that dehydroepiandrosterone could exert anti-fibrotic effects by affecting fibroblast migration, proliferation, differentiation, and collagen synthesis[40]. Previous Mendelian randomization studies have indicated that hypothyroidism promotes the development of IPF, possibly due to enhanced oxidative stress and impaired mitochondrial function in a hypothyroid state[41, 42]. We speculate that estrogen sulfotransferase sulfonates dehydroepiandrosterone and thyroid hormones, rendering them inactive, may be one of the reasons estrogen sulfotransferase promotes IPF development. This study is the first to utilize the MR method to assess the causal relationships between testosterone, estrogen, and estrogen sulfotransferase levels and the risk of IPF. Our MR analysis was based on large-sample GWAS data from European populations, providing sufficient statistical power for accurate estimation of causal effects. Additionally, our study effectively mitigated the potential for reverse causality and confounding factors. Nevertheless, there are some limitations to our study. Firstly, since the GWAS data used in our study were derived from European populations, the generalizability of our findings to other populations may be limited. Therefore, future research should include more diverse populations to validate and extend these findings. Secondly, due to constraints in the original data, our study did not perform detailed stratified analyses by gender and age. Future studies should consider these key demographic variables for a more comprehensive understanding of their impact on genetic risk for IPF. Conclusions Our study suggests that genetically predicted higher serum testosterone levels may be associated with a reduced risk of IPF, while an increase in serum estrogen sulfotransferase levels may potentially elevate the risk of IPF. No causal relationship was found between estradiol levels and the risk of IPF. Furthermore, we did not identify any causal relationships between IPF and testosterone, estradiol, or estrogen sulfotransferase. Declarations Ethics approval and consent to participate The data used in this paper are publicly available, ethically approved. Consent for publication Not applicable. Data Availability All GWAS data used in this study are available in the IEU Open GWAS Project (https://gwas.mrcieu.ac.uk/). Competing of interests The authors declare that they have no competing interests. Funding Not applicable. Contributions QX and QL conceived and designed the study. QX,GH,MW,KT,YZ,FC conducted data analysis. QX wrote the manuscript and revised the manuscript. 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Supplementary Files Supplementary.docx Cite Share Download PDF Status: Published Journal Publication published 03 Sep, 2024 Read the published version in BMC Pulmonary Medicine → Version 1 posted Editorial decision: Revision requested 18 Jun, 2024 Reviews received at journal 17 Jun, 2024 Reviews received at journal 12 Jun, 2024 Reviewers agreed at journal 08 Jun, 2024 Reviewers agreed at journal 15 Apr, 2024 Reviewers agreed at journal 05 Apr, 2024 Reviewers agreed at journal 18 Mar, 2024 Reviewers invited by journal 22 Feb, 2024 Editor assigned by journal 22 Feb, 2024 Editor invited by journal 16 Feb, 2024 Submission checks completed at journal 16 Feb, 2024 First submitted to journal 04 Feb, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3928046","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":273282127,"identity":"db059489-5245-4ebf-8672-f4bd8519928a","order_by":0,"name":"Qingying Xu","email":"","orcid":"","institution":"Fujian Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Qingying","middleName":"","lastName":"Xu","suffix":""},{"id":273282128,"identity":"bf78bfae-21f3-4249-a1c7-c4cfe5c0318a","order_by":1,"name":"Guangwang Hu","email":"","orcid":"","institution":"Fujian Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Guangwang","middleName":"","lastName":"Hu","suffix":""},{"id":273282129,"identity":"b652f891-2e72-43c3-aa2f-b85e1bb6b227","order_by":2,"name":"Qunying Lin","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAqklEQVRIiWNgGAWjYBACPmYQacAgx8befIA4LWxQLcZ8PMcSiNQCpRPnSeQoEKmFnffgbZ6CO+ltDDkMDD8qthHjML5kax6DZ7ltDGcPMPacuU2MFh4zaR6Dw7ltjH0JzIxtJGhJBzIMSNOSwMZGghZjyzkGhw3beNgSDhLlF37+M4Y33vw5LC8///HBBz8qiNACAlI8UMYB4tQDgeQPopWOglEwCkbBiAQAa+swH32UdTcAAAAASUVORK5CYII=","orcid":"","institution":"Fujian Medical University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Qunying","middleName":"","lastName":"Lin","suffix":""},{"id":273282130,"identity":"9e9f2a90-e2ee-44a8-82ba-474a19241ef3","order_by":3,"name":"Menghang Wu","email":"","orcid":"","institution":"Fujian Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Menghang","middleName":"","lastName":"Wu","suffix":""},{"id":273282131,"identity":"7d9b5b43-7811-4e69-b1c4-228295889964","order_by":4,"name":"Kenan Tang","email":"","orcid":"","institution":"Fujian Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Kenan","middleName":"","lastName":"Tang","suffix":""},{"id":273282132,"identity":"e1b4d67e-e38a-4635-830a-d97c47c42811","order_by":5,"name":"Yuyu Zhang","email":"","orcid":"","institution":"Fujian Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yuyu","middleName":"","lastName":"Zhang","suffix":""},{"id":273282133,"identity":"1f6d3c1d-020b-487d-b125-fce0ad8cc541","order_by":6,"name":"Feng Chen","email":"","orcid":"","institution":"Fujian Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Feng","middleName":"","lastName":"Chen","suffix":""}],"badges":[],"createdAt":"2024-02-04 15:29:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3928046/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3928046/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12890-024-03198-0","type":"published","date":"2024-09-03T16:05:49+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":51305614,"identity":"5df6edcd-2674-4637-a5fc-061ecbe115a0","added_by":"auto","created_at":"2024-02-19 08:45:11","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":127007,"visible":true,"origin":"","legend":"\u003cp\u003eThree assumptions for IVs in MR analysis.\u003c/p\u003e","description":"","filename":"Fig.1.png","url":"https://assets-eu.researchsquare.com/files/rs-3928046/v1/8f61092ffaf64b336da06944.png"},{"id":51305615,"identity":"dc8f19f5-f3fe-467e-8abe-89e820cb4e52","added_by":"auto","created_at":"2024-02-19 08:45:11","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":178164,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot showing results and sensibility analysis from MR study.\u003c/p\u003e","description":"","filename":"Fig.2.png","url":"https://assets-eu.researchsquare.com/files/rs-3928046/v1/24cff69490a31b50586a1121.png"},{"id":51305618,"identity":"108948fe-5833-4f10-98bd-9aacf05109c1","added_by":"auto","created_at":"2024-02-19 08:45:11","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":358384,"visible":true,"origin":"","legend":"\u003cp\u003eThe results of leave-one-out analysis for Testosterone, Estradiol, Estrogen Sulfotransferase and IPF in turn.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-3928046/v1/6851adde08499255369320c8.png"},{"id":51305616,"identity":"2272209e-55d1-41c3-b22b-357110c15132","added_by":"auto","created_at":"2024-02-19 08:45:11","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":171550,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot showing results and sensibility analysis from reverse MR study.\u003c/p\u003e","description":"","filename":"Fig.4.png","url":"https://assets-eu.researchsquare.com/files/rs-3928046/v1/b986ad442e59007a19ae66da.png"},{"id":51305617,"identity":"42b9a51f-53c5-4596-8e02-2a79f2771dbb","added_by":"auto","created_at":"2024-02-19 08:45:11","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":117692,"visible":true,"origin":"","legend":"\u003cp\u003eThe results of leave-one-out analysis for IPF in relation to Testosterone, Estradiol, and Estrogen Sulfotransferase in turn.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-3928046/v1/b766a97a2f0bf6a6bd984064.png"},{"id":64186043,"identity":"1e948c8a-3c3a-4949-8a7e-387b66b90711","added_by":"auto","created_at":"2024-09-09 16:24:02","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2291010,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3928046/v1/ca82d067-6092-4dc5-a370-7bc749383a69.pdf"},{"id":51305613,"identity":"b67fd87e-7c18-482f-ac50-aa88f5641121","added_by":"auto","created_at":"2024-02-19 08:45:11","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1339726,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementary.docx","url":"https://assets-eu.researchsquare.com/files/rs-3928046/v1/84eff03b98d3b3141698e9e5.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"The association between testosterone, estradiol, estrogen sulfotransferase and idiopathic pulmonary fibrosis: a bidirectional Mendelian randomization study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIdiopathic pulmonary fibrosis (IPF) is a chronic interstitial lung disease characterized by progressive and irreversible pulmonary fibrosis. The etiology and pathogenesis of IPF are complex and not yet fully understood. Risk factors for IPF include aging, smoking history, genetic factors, environmental exposure, lung microbiota, gastroesophageal reflux, and more[1]. It is currently believed that IPF is caused by persistent or repeated lung epithelial injury and subsequent activation of fibroblasts and myofibroblast differentiation[2]. Persistent myofibroblast expression leads to excessive extracellular matrix deposition, abnormal lung repair, tissue scarring, distortion of alveolar structure, and irreversible loss of lung function[2]. According to a study covering 12 countries, the adjusted incidence and prevalence rates of IPF are 0.09\u0026ndash;1.2/10,000 and 0.33\u0026ndash;4.51/10,000, respectively[3]. IPF predominantly affects males, with a French multicenter prospective study reporting that 78% of 236 newly diagnosed IPF patients were male, and 22% were female[4]. IPF progresses rapidly and has a poor prognosis. A systematic review and meta-analysis of 63,307 patients from 20 countries showed 3-year and 5-year survival rates of 61.8% (95% CI 58.7\u0026thinsp;~\u0026thinsp;64.9) and 45.6% (95% CI 41.5\u0026thinsp;~\u0026thinsp;49.7), respectively[5].\u003c/p\u003e \u003cp\u003eCurrently, FDA-approved drugs for treating IPF include Pirfenidone and Nintedanib, both of which can slow the progression of IPF but cannot reverse established pulmonary fibrosis and are associated with tolerability issues[6, 7]. Lung transplantation remains the best option for treating IPF, but it is plagued by issues such as donor shortage, surgical risks, infection risk, and rejection reactions. Therefore, exploring new treatment approaches for IPF is imperative.\u003c/p\u003e \u003cp\u003eSex hormones are a class of biological molecules produced by the endocrine system, primarily including androgens and estrogens. Testosterone and estradiol are the most prominent biologically active forms of androgens and estrogens, respectively[8, 9]. Sex hormones play important roles not only in sexual development and reproductive system function but also in influencing the immune system and metabolism. Estrogen sulfotransferase is the enzyme with the highest affinity for estrogens and is primarily responsible for catalyzing the sulfation of estrogens[10, 11]. Estrogen sulfotransferase has also been found to be involved in the sulfation of dehydroepiandrosterone and thyroid hormones[10].\u003c/p\u003e \u003cp\u003eSex hormones have been found to participate in the fibrotic processes in multiple organs, including the heart, kidneys, liver, and more[12\u0026ndash;16]. However, research on the role of sex hormones in IPF is limited, and some results are contradictory. A case-control study involving 101 male IPF patients and 51 healthy controls found a significant decrease in testosterone levels in the IPF group[17]. Furthermore, Hideki Nawa et al. found that anti-androgen drugs might lead to interstitial pneumonia, possibly by inhibiting the binding of androgens to receptors[18]. Regarding estradiol, L. Cody Smith and colleagues discovered that estradiol may reduce lung fibrosis occurrence by specifically downregulating the expression of CLIC 3 and RBP 7, genes associated with IPF, without affecting the TGF-β1 signaling pathway[19]. Research by Yonghong Xiao and others showed that estradiol could inhibit the development of lung fibrosis in mice by upregulating the expression of Caveolin-1 and suppressing the expression of type III collagen[20]. In addition, Solopov P. and colleagues' study indicated that dietary phytoestrogen intake could alleviate lung fibrosis in mice[21]. However, Mehrnaz Gharaee-Kermani and colleagues arrived at different conclusions, finding that female rats had more severe lung fibrotic responses than male rats, ovariectomy reduced lung fibrosis severity, and estradiol replacement therapy restored fibrotic responses, suggesting a potential pro-fibrotic role of estrogens[22]. Stevan P Tofovic and colleagues' study demonstrated that estrogen had an anti-mitotic effect on human lung fibroblasts only at high pharmacological concentrations (5\u0026micro;M) and had no effect on the growth of human lung fibroblasts at a concentration of 10\u0026micro;M[23]. In contrast, 2-methoxyestradiol (an endogenous metabolite of estradiol) inhibited the growth of human lung fibroblasts in a concentration-dependent manner[23]. Currently, research on the role of estrogen sulfotransferase in IPF is still lacking. Therefore, further studies are needed to explore the causal relationships between testosterone, estradiol, estrogen sulfotransferase, and IPF.\u003c/p\u003e \u003cp\u003eMendelian Randomization (MR) analysis is a research method in genetic epidemiology proposed by Professor Katan. The basic principle of MR is to use genetic variation as instrumental variables to infer causality between exposure and outcome [24]. Unlike traditional studies that can only discover associations between variables, MR can provide stronger evidence for causal inference. Furthermore, MR's greatest advantage is that genetic variation is randomly allocated at conception, reducing the possibility of reverse causation and the influence of confounding factors[25]. Compared to observational studies, which are susceptible to bias and confounding factors, and experimental studies, which are costly and subject to ethical limitations and participant compliance issues, MR offers a robust approach for causal inference. MR must satisfy three core assumptions[24]: the relevance assumption, where instrumental variables must be closely related to the exposure; the independence assumption, where instrumental variables are not influenced by potential confounding factors; and the exclusion restriction assumption, where genetic variation affects the outcome only through the exposure (Figure.1) .\u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThree assumptions for IVs in MR analysis.\u003c/p\u003e \u003cp\u003eIn this study, our data were derived from a genome-wide association study (GWAS), and we employed a two-sample bidirectional MR approach to investigate the causal relationships between testosterone, estradiol, estrogen sulfotransferase, and IPF.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003eStudy Design\u003c/h2\u003e\n \u003cp\u003eWe conducted a two-sample bidirectional MR study to explore the causal relationships between testosterone, estradiol, estrogen sulfotransferase and IPF. Causal effects were assessed using the IVW method, MR-Egger method, and weighted median method (WME). We also performed Cochran\u0026apos;s Q test to assess heterogeneity and used MR-Egger regression and MR-PRESO method to examine pleiotropy. Additionally, we conducted a leave-one-out analysis to assess the robustness of our findings.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003eData Sources\u003c/h2\u003e\n \u003cp\u003eIn this study, genetic data for testosterone, estradiol, estrogen sulfotransferase, and IPF were obtained from the IEU GWAS database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://gwas.mrcieu.ac.uk/\u003c/span\u003e\u003c/span\u003e). All data used in this study are in the public domain and do not require additional ethical approval. Study populations were of European ancestry to minimize potential bias due to racial factors. The dataset for testosterone (GWAS ID: ebi-a-GCST90014013) included 353,805 samples and 10,783,644 single nucleotide polymorphisms (SNPs). The dataset for estradiol (GWAS ID: ebi-a-GCST90020092) consisted of 206,927 samples and 16,136,413 SNPs. The dataset for estrogen sulfotransferase (GWAS ID: prot-a-2892) included 3,301 samples and 10,534,735 SNPs. The dataset for IPF (GWAS ID: finn-b-IPF) comprised 198,014 samples and 16,380,413 SNPs (Table.1).\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eTable.1\u0026nbsp;\u003c/strong\u003eInformation of the exposures and outcome datasets.\u003c/p\u003e\n \u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"614\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.107491856677523%\" valign=\"top\"\u003e\n \u003cp\u003ePhenotype\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.710097719869708%\" valign=\"top\"\u003e\n \u003cp\u003eIEU GWAS id\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.19543973941368%\" valign=\"top\"\u003e\n \u003cp\u003eSample size\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.98697068403909%\" valign=\"top\"\u003e\n \u003cp\u003eNumbers of SNPs\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.107491856677523%\" valign=\"top\"\u003e\n \u003cp\u003eTestosterone\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.710097719869708%\" valign=\"top\"\u003e\n \u003cp\u003eebi-a-GCST90014013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.19543973941368%\" valign=\"top\"\u003e\n \u003cp\u003e353,805\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.98697068403909%\" valign=\"top\"\u003e\n \u003cp\u003e10,783,644\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.107491856677523%\" valign=\"top\"\u003e\n \u003cp\u003eEstradiol\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.710097719869708%\" valign=\"top\"\u003e\n \u003cp\u003eebi-a- GCST90020092\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.19543973941368%\" valign=\"top\"\u003e\n \u003cp\u003e163,985\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.98697068403909%\" valign=\"top\"\u003e\n \u003cp\u003e7,488,193\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.107491856677523%\" valign=\"top\"\u003e\n \u003cp\u003eEstrogen sulfotransferase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.710097719869708%\" valign=\"top\"\u003e\n \u003cp\u003eprot-a-2892\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.19543973941368%\" valign=\"top\"\u003e\n \u003cp\u003e3,301\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.98697068403909%\" valign=\"top\"\u003e\n \u003cp\u003e10,534,735\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.107491856677523%\" valign=\"top\"\u003e\n \u003cp\u003eIPF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.710097719869708%\" valign=\"top\"\u003e\n \u003cp\u003efinn-b-IPF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.19543973941368%\" valign=\"top\"\u003e\n \u003cp\u003e198,014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.98697068403909%\" valign=\"top\"\u003e\n \u003cp\u003e16,380,413\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eSNP single nucleotide polymorphism.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003eInstrumental Variables\u003c/h2\u003e\n \u003cp\u003eTo ensure a strong correlation between genetic variation and exposure, we extracted instrumental variables for testosterone, estrogen sulfotransferase, and IPF with a threshold of P\u0026thinsp;\u0026lt;\u0026thinsp;5\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e and for estradiol with a threshold of P\u0026thinsp;\u0026lt;\u0026thinsp;5\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;7\u003c/sup\u003e (only 2 SNPs were obtained with P\u0026thinsp;\u0026lt;\u0026thinsp;5\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e for estradiol). Furthermore, to mitigate linkage disequilibrium (LD), we set clustering thresholds at r\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001 and a minimum intergenic distance of \u0026gt;\u0026thinsp;10,000 kb. We then excluded SNPs with an F-statistic less than 10, calculated using the formula F\u0026thinsp;=\u0026thinsp;R\u003csup\u003e2\u003c/sup\u003e(n-k-1)/k(1-R\u003csup\u003e2\u003c/sup\u003e), to avoid weak instrumental variables. Additionally, we ensured data harmonization by removing palindromic SNPs. Lastly, we performed a global outlier test using MR-PRESO and removed any outliers.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n \u003ch2\u003eMR Analysis\u003c/h2\u003e\n \u003cp\u003eIn this study, we used R software version 4.3.2 and conducted the analysis using the TwoSampleMR package and MR-PRESO package. Causal effects were assessed using the IVW method, MR-Egger method, and WME method. The IVW method estimates the final causal effect by calculating the weighted average of the effect sizes and standard errors of each genetic variant, thereby reducing the impact of genetic variants with larger measurement errors[26]. The MR-Egger method accounts for the presence of an intercept and can be used to assess pleiotropy[27]. The WME method assumes that over half of the instrumental variables are valid, weights each instrumental variable\u0026apos;s effect by its precision, and then calculates the median[28]. IVW is a robust method that fully utilizes all instrumental variables and offers higher statistical power compared to other methods. Therefore, in this study, we adopted IVW as the primary method for evaluating causal effects, with the other methods used for result validation.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003eSensitivity Analysis\u003c/h2\u003e\n \u003cp\u003eWe conducted Cochran\u0026apos;s Q test to assess heterogeneity, used MR-Egger regression and MR-PRESO method to examine pleiotropy, and performed a leave-one-out analysis to assess robustness. A Cochran Q test with P\u0026thinsp;\u0026gt;\u0026thinsp;0.05 indicates no heterogeneity. If the MR Egger intercept has a P\u0026thinsp;\u0026gt;\u0026thinsp;0.05, it suggests no horizontal pleiotropy. If the MR-PRESO Global test has P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, it indicates the presence of horizontal pleiotropy. MR-PRESO also detects outlier SNPs, which we removed before re-conducting MR analysis. The leave-one-out analysis involves systematically removing each SNP and computing the remaining results to assess the impact of the excluded SNP on causal effects.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eMR Results and Sensitivity Analysis for Testosterone, Estradiol, Estrogen Sulfotransferase and IPF\u003c/h2\u003e \u003cp\u003eGenetically predicted serum testosterone levels were associated with a 58.7% reduced risk of IPF for everyone standard deviation increase (OR\u0026thinsp;=\u0026thinsp;0.413, P\u003csub\u003eIVW\u003c/sub\u003e=0.029, 95% CI\u0026thinsp;=\u0026thinsp;0.187\u0026thinsp;~\u0026thinsp;0.912), while genetically predicted estrogen sulfotransferase levels were associated with a 32.4% increased risk of IPF for everyone standard deviation increase (OR\u0026thinsp;=\u0026thinsp;1.324, P\u003csub\u003eIVW\u003c/sub\u003e=0.006, 95% CI\u0026thinsp;=\u0026thinsp;1.083\u0026thinsp;~\u0026thinsp;1.618). Leave-one-out analysis for estradiol revealed that rs2345568 had a significant impact on the results and was removed. No causal relationship was found between estradiol and IPF (OR\u0026thinsp;=\u0026thinsp;1.094, P\u003csub\u003eIVW\u003c/sub\u003e=0.735, 95% CI\u0026thinsp;=\u0026thinsp;0.650\u0026thinsp;~\u0026thinsp;1.841).\u003c/p\u003e \u003cp\u003eCochran's Q test for IVW on testosterone, estradiol, and estrogen sulfotransferase had P-values of 0.24, 0.88, and 0.646, respectively, all greater than 0.05, indicating no significant heterogeneity. MR-Egger intercepts were 0.178, 0.83, and 0.87 for testosterone, estradiol, and MR-PRESO Global test P-values were 0.262 and 0.871 for testosterone and estradiol, respectively, all greater than 0.05. MR-Egger and MR-PRESO methods suggested no evidence of potential horizontal pleiotropy. The Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the results. Leave-one-out analysis showed no statistically significant differences in the effect estimates for each SNP( Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The scatter plots depict the estimated impact of IVs on exposure and outcomes (Supplementary Fig.\u0026nbsp;1). Forest plots, Funnel plots and Density plot can be found in supplementary Figs.\u0026nbsp;2\u0026ndash;4.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eForest plot showing results and sensibility analysis from MR study.\u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe results of leave-one-out analysis for Testosterone, Estradiol, Estrogen Sulfotransferase and IPF in turn.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eMR Results and Sensitivity Analysis for IPF in relation to Testosterone, Estradiol, and Estrogen Sulfotransferase\u003c/h2\u003e \u003cp\u003eNo causal relationships were observed between IPF and testosterone (OR\u0026thinsp;=\u0026thinsp;1.001, P\u003csub\u003eIVW\u003c/sub\u003e=0.51, 95% CI\u0026thinsp;=\u0026thinsp;0.998\u0026thinsp;~\u0026thinsp;1.004), estradiol (OR\u0026thinsp;=\u0026thinsp;1.001, P\u003csub\u003eIVW\u003c/sub\u003e=0.958, 95% CI\u0026thinsp;=\u0026thinsp;0.982\u0026thinsp;~\u0026thinsp;1.019), or estrogen sulfotransferase (OR\u0026thinsp;=\u0026thinsp;0.975, P\u003csub\u003eIVW\u003c/sub\u003e=0.251, 95% CI\u0026thinsp;=\u0026thinsp;0.933\u0026thinsp;~\u0026thinsp;1.018).\u003c/p\u003e \u003cp\u003eCochran's Q test for IVW on IPF with estradiol and estrogen sulfotransferase had P-values of 0.53 and 0.29, respectively, both greater than 0.05, indicating no significant heterogeneity. However, for IPF and testosterone, the IVW method had a Cochran's Q test P-value of 0.04, suggesting the presence of heterogeneity. MR-Egger intercepts for IPF and testosterone, estradiol, and estrogen sulfotransferase were 0.78, 0.88, and 0.9, respectively, all greater than 0.05. MR-PRESO Global test P-values for IPF and testosterone, estradiol, and estrogen sulfotransferase were 0.169, 0.673, and 0.419, respectively. MR-Egger and MR-PRESO methods indicated no evidence of potential horizontal pleiotropy. The Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows the results. Leave-one-out analysis showed no statistically significant differences in the effect estimates for each SNP ( Figs.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The scatter plots depict the estimated impact of IVs on exposure and outcomes (Supplementary Fig.\u0026nbsp;5). Forest plots, Funnel plots and Density plot can be found in supplementary Figs.\u0026nbsp;6\u0026ndash;8.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eForest plot showing results and sensibility analysis from reverse MR study.\u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe results of leave-one-out analysis for IPF in relation to Testosterone, Estradiol, and Estrogen Sulfotransferase in turn.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur study employed a two-sample bidirectional Mendelian randomization (MR) approach to investigate the associations between testosterone, estradiol, estrogen sulfotransferase levels, and the risk of IPF (IPF). We observed that higher serum testosterone levels were associated with a decreased risk of IPF, while an increase in serum estrogen sulfotransferase levels may potentially elevate the risk of IPF. However, no causal relationship was found between estradiol levels and the occurrence of IPF. In the reverse MR analysis, we did not find any causal relationships between IPF and testosterone, estradiol, or estrogen sulfotransferase.\u003c/p\u003e \u003cp\u003ePrior studies have suggested a potential role for sex hormones in the development of IPF, but findings have been inconsistent, and the exact mechanisms are still debated. Our MR results contribute new evidence to this ongoing debate. Our findings may shed light on the protective role of testosterone against IPF. Previous studies have reported significantly reduced testosterone levels in IPF patients, with a positive correlation between testosterone levels and telomere length, a common susceptibility factor in sporadic and familial IPF[17, 29]. Telomere length has been inversely associated with IPF development in previous Mendelian randomization studies[30], suggesting that modulation of telomere length could be one of the mechanisms through which testosterone influences IPF. IPF predominantly affects elderly males, and the decline in testosterone levels with aging may be associated with this trend. TGF-β plays a crucial role in IPF pathogenesis by stimulating fibroblast activation and proliferation and is one of the targets of the anti-fibrotic drug Pirfenidone [31, 32]. Jia et al. demonstrated that testosterone propionate (an exogenous androgen) could improve renal fibrosis in aged rats by inhibiting the TGF-β1/Smad pathway and activating the Nrf2-ARE signaling pathway[16]. The renin-angiotensin system (RAS) is also involved in the pathogenesis of IPF, with ACE-AngⅡ-AT1R promoting tissue fibrosis and ACE2-Ang(1\u0026ndash;7)-AT2R antagonizing fibrosis progression[33]. Despite increased AT2R expression in IPF patients, the AT1R pro-fibrotic axis still predominates. Yang et al. found that testosterone could inhibit Ang II-induced excessive proliferation and collagen synthesis in cardiac fibroblasts by suppressing the ERK1/2 pathway[15]. From these studies, we can infer that the protective role of testosterone in IPF may be associated with the inhibition of the TGF-β and Ang II pathways. Testosterone also possesses immunomodulatory properties, suppressing inflammation by increasing anti-inflammatory cytokine IL-10 and reducing pro-inflammatory cytokines TNFα, IL-1β, and IL-6[34]. IL-6 not only participates in IPF-associated inflammatory responses but also promotes fibroblast proliferation[35]. Furthermore, studies have shown that testosterone can improve mitochondrial function in heart, muscle, and brain tissues[36\u0026ndash;38], while mitochondrial dysfunction plays a significant role in IPF pathogenesis[39].\u003c/p\u003e \u003cp\u003eCriselda Mendoza-Milla et al. found that dehydroepiandrosterone could exert anti-fibrotic effects by affecting fibroblast migration, proliferation, differentiation, and collagen synthesis[40]. Previous Mendelian randomization studies have indicated that hypothyroidism promotes the development of IPF, possibly due to enhanced oxidative stress and impaired mitochondrial function in a hypothyroid state[41, 42]. We speculate that estrogen sulfotransferase sulfonates dehydroepiandrosterone and thyroid hormones, rendering them inactive, may be one of the reasons estrogen sulfotransferase promotes IPF development.\u003c/p\u003e \u003cp\u003eThis study is the first to utilize the MR method to assess the causal relationships between testosterone, estrogen, and estrogen sulfotransferase levels and the risk of IPF. Our MR analysis was based on large-sample GWAS data from European populations, providing sufficient statistical power for accurate estimation of causal effects. Additionally, our study effectively mitigated the potential for reverse causality and confounding factors. Nevertheless, there are some limitations to our study. Firstly, since the GWAS data used in our study were derived from European populations, the generalizability of our findings to other populations may be limited. Therefore, future research should include more diverse populations to validate and extend these findings. Secondly, due to constraints in the original data, our study did not perform detailed stratified analyses by gender and age. Future studies should consider these key demographic variables for a more comprehensive understanding of their impact on genetic risk for IPF.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eOur study suggests that genetically predicted higher serum testosterone levels may be associated with a reduced risk of IPF, while an increase in serum estrogen sulfotransferase levels may potentially elevate the risk of IPF. No causal relationship was found between estradiol levels and the risk of IPF. Furthermore, we did not identify any causal relationships between IPF and testosterone, estradiol, or estrogen sulfotransferase.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data used in this paper are publicly available, ethically approved.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll GWAS data used in this study are available in the IEU Open GWAS Project (https://gwas.mrcieu.ac.uk/).\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting of interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eQX and QL conceived and designed the study. QX,GH,MW,KT,YZ,FC conducted data analysis. QX wrote the manuscript and revised the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe express our gratitude to the IEU Open GWAS database for providing publicly available summary-level GWAS data for our study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBarratt SL, Creamer A, Hayton C, Chaudhuri N: \u003cstrong\u003eIdiopathic Pulmonary Fibrosis (IPF): An Overview\u003c/strong\u003e. \u003cem\u003eJ Clin Med \u003c/em\u003e2018, \u003cstrong\u003e7\u003c/strong\u003e(8).\u003c/li\u003e\n\u003cli\u003eMei Q, Liu Z, Zuo H, Yang Z, Qu J: \u003cstrong\u003eIdiopathic Pulmonary Fibrosis: An Update on Pathogenesis\u003c/strong\u003e. \u003cem\u003eFront Pharmacol \u003c/em\u003e2021, \u003cstrong\u003e12\u003c/strong\u003e:797292.\u003c/li\u003e\n\u003cli\u003eMaher TM, Bendstrup E, Dron L, Langley J, Smith G, Khalid JM, Patel H, Kreuter M: \u003cstrong\u003eGlobal incidence and 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C, Chavira R, Ruiz V, Barrera L, Monta\u0026ntilde;o M\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eDehydroepiandrosterone has strong antifibrotic effects and is decreased in idiopathic pulmonary fibrosis\u003c/strong\u003e. \u003cem\u003eEur Respir J \u003c/em\u003e2013, \u003cstrong\u003e42\u003c/strong\u003e(5):1309-1321.\u003c/li\u003e\n\u003cli\u003eZhang Y, Zhao M, Guo P, Wang Y, Liu L, Zhao J, Gao L, Yuan Z, Xue F, Zhao J: \u003cstrong\u003eMendelian randomisation highlights hypothyroidism as a causal determinant of idiopathic pulmonary fibrosis\u003c/strong\u003e. \u003cem\u003eEBioMedicine \u003c/em\u003e2021, \u003cstrong\u003e73\u003c/strong\u003e:103669.\u003c/li\u003e\n\u003cli\u003eZhu J, Zhou D, Wang J, Yang Y, Chen D, He F, Li Y: \u003cstrong\u003eA Causal Atlas on Comorbidities in Idiopathic Pulmonary Fibrosis: A Bidirectional Mendelian Randomization Study\u003c/strong\u003e. \u003cem\u003eChest \u003c/em\u003e2023, \u003cstrong\u003e164\u003c/strong\u003e(2):429-440.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-pulmonary-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pulm","sideBox":"Learn more about [BMC Pulmonary Medicine](http://bmcpulmmed.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pulm/default.aspx","title":"BMC Pulmonary Medicine","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Idiopathic pulmonary fibrosis, Sex hormones, Testosterone, Estrogen sulfotransferase, Mendelian randomization","lastPublishedDoi":"10.21203/rs.3.rs-3928046/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3928046/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe causal relationships between testosterone, estradiol, estrogen sulfotransferase, and idiopathic pulmonary fibrosis (IPF) are not well understood. This study employs a bidirectional two-sample Mendelian Randomization (MR) approach to explore these associations.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe extracted significant genetic loci associated with testosterone, estradiol, and estrogen sulfotransferase from GWAS summary data as instrumental variables, with IPF as the outcome variable for a two-sample Mendelian randomization analysis. Instrumental variables and outcome variables were then swapped for a bidirectional two-sample Mendelian randomization analysis. The inverse variance weighted (IVW), MR-Egger, and weighted median methods were used to evaluate causal relationships. Cochran's Q test, MR-Egger regression, MR-PRESSO global test, and leave-one-out method were used for sensitivity analyses.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eGenetically predicted increases in serum testosterone levels by one standard deviation were associated with a 58.7% decrease in the risk of developing IPF (OR\u0026thinsp;=\u0026thinsp;0.413, P\u003csub\u003eIVW\u003c/sub\u003e=0.029, 95% CI\u0026thinsp;=\u0026thinsp;0.187\u0026thinsp;~\u0026thinsp;0.912), while an increase in serum estrogen sulfotransferase by one standard deviation was associated with a 32.4% increase in risk (OR\u0026thinsp;=\u0026thinsp;1.324, P\u003csub\u003eIVW\u003c/sub\u003e=0.006, 95% CI\u0026thinsp;=\u0026thinsp;1.083\u0026thinsp;~\u0026thinsp;1.618). No causal relationship was found between estradiol (OR\u0026thinsp;=\u0026thinsp;1.094, P\u003csub\u003eIVW\u003c/sub\u003e=0.735, 95% CI\u0026thinsp;=\u0026thinsp;0.650\u0026thinsp;~\u0026thinsp;1.841) and the risk of IPF. Reverse MR analysis did not reveal any causal relationship between IPF and testosterone (OR\u0026thinsp;=\u0026thinsp;1.001, P\u003csub\u003eIVW\u003c/sub\u003e=0.51, 95% CI\u0026thinsp;=\u0026thinsp;0.998\u0026thinsp;~\u0026thinsp;1.004), estradiol (OR\u0026thinsp;=\u0026thinsp;1.001, P\u003csub\u003eIVW\u003c/sub\u003e=0.958, 95% CI\u0026thinsp;=\u0026thinsp;0.982\u0026thinsp;~\u0026thinsp;1.019), or estrogen sulfotransferase (OR\u0026thinsp;=\u0026thinsp;0.975, P\u003csub\u003eIVW\u003c/sub\u003e=0.251, 95% CI\u0026thinsp;=\u0026thinsp;0.933\u0026thinsp;~\u0026thinsp;1.018).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eIncreased serum levels of testosterone are associated with a reduced risk of IPF, while increased levels of serum estrogen sulfotransferase are associated with an increased risk. No causal relationship was found between estradiol and the development of IPF. No causal relationship was identified between IPF and testosterone, estradiol, or estrogen sulfotransferase.\u003c/p\u003e","manuscriptTitle":"The association between testosterone, estradiol, estrogen sulfotransferase and idiopathic pulmonary fibrosis: a bidirectional Mendelian randomization study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-19 08:45:06","doi":"10.21203/rs.3.rs-3928046/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-06-18T05:55:53+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-06-17T13:39:26+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-06-12T12:04:22+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"117356363081423298739165996043034988593","date":"2024-06-08T09:36:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"8d841118-6955-4634-9977-a9f9f68e7a75","date":"2024-04-15T18:00:04+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"28bf6ca2-6828-49a6-93c7-f4e520fa9742","date":"2024-04-05T09:29:00+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"3a6bbbb8-0879-4308-bbe2-16f2732a9f84","date":"2024-03-18T06:30:09+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-02-22T10:34:56+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-02-22T10:19:03+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-02-16T17:45:26+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-02-16T15:50:33+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Pulmonary Medicine","date":"2024-02-04T15:23:55+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-pulmonary-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pulm","sideBox":"Learn more about [BMC Pulmonary Medicine](http://bmcpulmmed.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pulm/default.aspx","title":"BMC Pulmonary Medicine","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9d1d43ab-c46c-4c50-ac89-14e90e162218","owner":[],"postedDate":"February 19th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-09-09T16:15:21+00:00","versionOfRecord":{"articleIdentity":"rs-3928046","link":"https://doi.org/10.1186/s12890-024-03198-0","journal":{"identity":"bmc-pulmonary-medicine","isVorOnly":false,"title":"BMC Pulmonary Medicine"},"publishedOn":"2024-09-03 16:05:49","publishedOnDateReadable":"September 3rd, 2024"},"versionCreatedAt":"2024-02-19 08:45:06","video":"","vorDoi":"10.1186/s12890-024-03198-0","vorDoiUrl":"https://doi.org/10.1186/s12890-024-03198-0","workflowStages":[]},"version":"v1","identity":"rs-3928046","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3928046","identity":"rs-3928046","version":["v1"]},"buildId":"cTy_lsJlmDsVRNrSptgXS","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}