Sex hormones and meningiomas risk: a Mendelian randomization and immunohistochemical analysis

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To further explore the causal relationship between sex hormones and the risk of meningioma, we employed Mendelian randomization (MR) and immunohistochemistry for causal inference analysis. Methods We obtained GWAS data relevant to meningioma from FinnGen, which included 314,708 participants. We found substantial evidence suggesting a potential causal relationship between PRLR, IGF, LH and the risk of meningioma using MR method. Results We included 16 cases of meningioma, of which 4 were associated with pregnancy. The average age of meningioma patients with pregnancy was significantly younger at 32.50 ± 1.29 years compared to those without pregnancy (60.58 ± 14.91 years; p = 0.002). Additionally, the proportion of grade II cases among those with pregnancy was found to be significantly higher at 100% compared to only 16.7% in those without pregnancy. The H-score of PRLR, IGF and LH in pregnancy group was significantly higher than that in without pregnancy group (< 0.001). While, there was no significant difference of H-score between two groups in other sex hormones. Conclusion The mendelian randomization analysis and immunohistochemical analysis demonstrated that PRLR, IGF and LH played an important role in the progression of meningioma. The targeted drug for PRLR, IGF and LH may become novel therapeutic methods for meningioma in the future. meningioma sex hormones Mendelian randomization immunohistochemistry Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction Meningiomas are common, slow-growing primary intracranial neoplasms, which occur in individuals during their fifth and sixth decades of life, with a notable female predominance (female-to-male ratio of 2:1)[ 1 , 2 ]. Previous studies reported cases in which meningiomas progressed and manifested symptoms during pregnancy, experienced remission postpartum[ 3 – 5 ]. The growth dynamics of meningiomas may be influenced by changes of sex steroid hormones during pregnancy. Previous vitro studies indicate that both progesterone and estrogen can independently stimulate meningioma proliferation[ 6 , 7 ]. Several studies have demonstrated that meningioma exhibit immunoreactivity for both (progesterone receptor) PgR and (estrogen receptor) ER receptors[ 6 , 8 ]. Therefore, pregnancy may significantly impact the progression of meningioma. Although there is evidence suggesting a link between sex hormones and progression of meningioma, the specific mechanisms underlying tumor growth during pregnancy remain poorly understood. Surgical excision continues to be the standard treatment modality for meningiomas; however, complications arise when addressing recurrent tumors or those resistant to radiation therapy which hinder complete surgical removal efforts[ 9 , 10 ]. In neoplasms cases where hormone receptors are present within the tumor tissue, additional antihormonal targeted therapies could offer significant benefits for affected patients[ 11 ]. To further explore the causal relationship between sex hormones and the risk of meningioma, we employed Mendelian randomization (MR) and immunohistochemistry for causal inference analysis. To corroborate our MR findings, we performed an immunohistochemical study involving a cohort consisting of 16 cases of meningioma. Furthermore, we examined differences in immunohistochemistry related to sex hormone receptors alongside clinical characteristics between pregnant women and non-pregnant individuals. 2. Materials And Methods 2.1 Study Design In order to infer the causal effects of exposures on outcomes, the MR analysis utilizes the random allocation of genetic variants at conception. In our study, the exposures comprised a range of sex hormones, while the outcome was meningioma. The evaluation of causal effects was performed within a univariable two-sample MR framework. As previous studies showed that the instrumental variables (IVs) must satisfy three essential assumptions: (i) IV is associated with exposure; (ii) IV is not associated with confounders that influence both exposure and outcome; and (iii) IV does not have a direct association with the outcome[ 12 , 13 ]. 2.2 Data sources We gathered genetic data from three GWAS databases: Finn Gens database, GWAS Catalog, and IEU Open GWAS, as detailed in Supplementary Table 1. We obtained GWAS data relevant to meningioma from FinnGen, which included 314,708 participants ( https://storage.googleapis.com/finngen-public-data-r10/summary_stats/finngen_R10_C3_MENINGIOMA_EXALLC.gz ). Additionally, summaries for PRLR (prolactin receptor), ER (estrogen receptor), FSH (follicle-stimulating hormone), GHR (growth hormone receptor), HCG (Human Chorionic Gonadotropin), IGF (Insulin-Like Growth Factor), LH (Luteinizing Hormone), TSH (Thyroid Stimulating Hormone), and PR (progesterone receptor) were acquired from "IEU OpenGWAS," whereas Thyroxine's GWAS summary was retrieved from the GWAS Catalog (GCST90266075). For all datasets analyzed, we compiled information on SNPs including effect allele frequency (EAF), effect allele (EA), effect size(β), and p-value. No additional ethical approval was required because that all data were publicly accessible. 2.3 Selection of instrument variables Initially, genetic polymorphisms exhibiting a significant association with meningioma on a genome-wide scale (p < 5 × 10 − 10 ) were employed as instrumental variables. We established an r-squared threshold of less than 0.0001 to reduce potential bias stemming from linkage disequilibrium (LD) and ensure the independence of these IVs. The single nucleotide polymorphisms (SNPs) that displayed a substantial correlation with exposure factors were identified using the F-statistic for SNPs, where strong IVs were characterized by an F value exceeding 10. Information pertaining to sex hormones and meningioma outcomes was extracted through GWAS to investigate the relationship between SNPs fulfilling Hypotheses 1, 2, and 3 and their corresponding outcomes. Finally, data obtained from both exposure and outcome variable databases were integrated and organized; palindromic sequences were removed to ensure consistency in effector alleles across all SNPs. 2.4 Mendelian randomization analysis We conducted a two-sample MR analysis utilizing five analytical techniques: weighted median (WME), inverse variance weighted (IVW), simple mode, MR-Egger regression, and weighted mode. Cochran’s Q test was employed to evaluate heterogeneity across individual genetic variance estimates. We implemented a random effects model within IVW for our final MR analysis, if the p-value < 0.05. A threshold of less than 0.05 for p-values was established as a demarcation point to differentiate statistically significant differences from those deemed statistically insignificant. The results were visualized through scatter plots and forest plots to depict the relationship between sex hormones and meningioma incidence. We also used the forest plots and funnel plots to evaluated the heterogeneity and stability of our results. The results of IVW method were scrutinized using multinomial residuals and outlier detection via the MR-PRESSO technique to ensure robust and precise interpretations of underlying relationships. 2.5 Tissue samples This investigation represents a retrospective analysis conducted from January 2010 to October 2024, concentrating on primary intracranial meningiomas diagnosed in the pathology department. Specimens were obtained from four cases of meningioma that either manifested or progressed during pregnancy or the postpartum period, in addition to twelve cases of meningioma unrelated to pregnancy, sourced from Qingdao Municipal Hospital. Clinical data for the patients were retrieved from the computerized hospital information system. Tumor tissue samples fixed in 10% neutral-buffered formalin were sectioned into slices with a thickness of 4–5 µm. Meningioma grading and histological classification were performed according to the WHO classification established in 2021. Immunohistochemical staining for sex hormone receptors was executed using a polymer-horseradish peroxidase (HRP) method. The study was approved by the Ethics Committee of Qingdao Hospital, University of Health and Rehabilitation Sciences (Qingdao Municipal Hospital) (Approval No.2025-KY-003). Because the study was retrospective and observational, the board waived the patients’ informed consent. 2.6 Immunohistochemistry procedure Sections of 4 µm thick, formalin-fixed, paraffin-embedded tissue were prepared on slides that had been pre-coated with 3-aminopropyltriethoxysilane. The slides were incubated overnight at a temperature of 37°C. Endogenous peroxidase activity was blocked using 3% hydrogen peroxide (H 2 O 2 ). Following this step, the sections were rinsed in Tris buffer (pH 7.6) and incubated for 30 minutes at room temperature with various antibodies: anti-PRLR (Rabbit polyclonal antibody, Q08501, BOSTER, Wuhan City, China), anti-ER (Rabbit polyclonal antibody, P03372, BOSTER), anti-FSH (Rabbit polyclonal antibody, P23945, BOSTER), anti-GHR (Rabbit polyclonal antibody AB_2840000 from Affinity Biosciences in Jiangsu), anti-HCG (Rabbit polyclonal antibody P03372 from BOSTER), anti-IGF (Rabbit polyclonal antibody P08069 from BOSTER), anti-LH (Rabbit polyclonal antibody P22888 from BOSTER), anti-Thyroxine (Rabbit polyclonal antibody P16473 from ZEN-BIOSCIENCE in Beijing), anti-TSH (Rabbit polyclonal antibody AB_2766360 from Wuhan City), and anti-PR (Rabbit polyclonal antibody P06401 sourced from both BOSTER and ABclonal Biotechnology Co., Ltd., Wuhan City). The slides were then treated with polymer-HRP reagent and diaminobenzidine chromogen after being washed again in Tris buffer. Counterstaining was conducted using Harris’s hematoxylin followed by dehydration and mounting with dibutyl phthalate xylene. Three distinct fields of view at a magnification of 100x were captured for each section utilizing scanning equipment: Aperio Versa 8 manufactured by Leica. These images underwent analysis through the Image Scope software module provided by Leica. The software is programmed to classify all deep brown areas on the tissue sections as strongly positive; light brown areas as moderately positive; pale yellow areas as weakly positive; and blue cell nuclei as negative. Subsequently, the software identifies each point within the tissue to calculate the area occupied by strong positives, moderate positives, weak positives, and negatives measured in pixels along with their respective percentages before calculating the histochemistry score known as H-score using the formula: H-SCORE = [(Strong positive percentage) x 3] + [(Medium positive percentage) x 2] + [(Weak positive percentage) x 1]. 2.7 Statistical analysis The age and H-score were reported as percentages along with the mean ± standard deviation. The WHO grade was similarly represented in terms of percentages. For group comparisons involving categorical variables, chi-square tests or Fisher’s exact tests were utilized, whereas t-tests or Mann-Whitney U tests were employed for continuous variables. A p-value of less than 0.05 was considered statistically significant. Statistical analyses were performed using R version 3.4.3 (R Foundation). 3. Results 3.1 Demographic characteristics The clinical characteristics are summarized in Table 1 . Our study included 16 cases of meningioma, of which 4 were associated with pregnancy. The mean age of the 16 meningioma patients was 53.56 ± 17.92 years. Among these, there were 10 cases (62.5%) classified as grade I and 6 cases (37.5%) classified as grade II. Details regarding basal immunohistochemistry for CK, Ema, Stat6, Ki67, and Sstr2 are presented in Table 1 . The average age of meningioma patients with pregnancy was significantly younger at 32.50 ± 1.29 years compared to those without pregnancy (60.58 ± 14.91 years; p = 0.002). Additionally, the proportion of grade II cases among those with pregnancy was found to be significantly higher at 100% compared to only 16.7% in those without pregnancy. Table 1 Characteristics of patients of meningioma Items Training cohort (N = 16) Age(years) 53.56 ± 17.92 Grade I 10(62.5%) II 6(37.5%) III 0 Pregnancy(n, %) Yes 4(25.0%) No 12(75.0%) IHC ( n, %) CK (+) 1(6.25%) Ema (+) 11(68.75%) Vimentin (+) 16(100.0%) Stat6 (+) 0 S100 (+) 4(25.0%) Ki67 (+,%) 4.56 ± 2.63 Gfap (+) 4(25.0%) Sstr2 (+) 10(62.5%) 3.2 Causal effects of sex hormones on meningioma We found substantial evidence suggesting a potential causal relationship between PRLR and the risk of meningioma, as demonstrated by several methods: Weighted median (OR = 1.50, SE = 0.067, P < 0.001), Inverse variance weighted (OR = 1.44, SE = 0.051, P < 0.001), Simple mode (OR = 1.5, SE = 0.11, P = 0.0011), and Weighted mode (OR = 1.51, SE = 0.11, P = 0.0019). In contrast, the MR-Egger approach produced opposing results (OR = 0.76, SE = 0.26, p = 0.29). Heterogeneity was evaluated using Cochran's Q-test with resulting p-values of 0.99 for MR-Egger and 0.93 for IVW (Table 2 ). Table 2 The immunohistochemical results of Meningioma cases Items Meningioma cases (n = 16) P Without pregnancy (n = 12) With pregnancy (n = 4) Age(years) 60.58 ± 14.91 32.50 ± 1.29 0.002 Grade 0.003 I 10(83.3%) 0 II 2(16.7%) 4(100%) III 0 0 IHC PRLR 73.28 ± 18.74 120.35 ± 12.46 < 0.001 ER 36.06 ± 21.44 19.20 ± 2.44 0.147 FSH 14.87 ± 3.01 17.31 ± 4.87 0.247 GHR 11.37 ± 1.18 14.45 ± 3.64 0.189 HCG 11.36 ± 2.67 11.38 ± 1.38 0.985 IGF 63.59 ± 6.63 126.00 ± 4.01 < 0.001 LH 49.81 ± 8.51 122.31 ± 7.89 < 0.001 Thyroxine 24.36 ± 6.07 18.88 ± 2.59 0.107 TSH 8.27 ± 1.04 7.67 ± 1.30 0.363 PR 18.98 ± 4.11 16.66 ± 4.01 0.344 Furthermore, we identified compelling evidence indicating a possible causal effect of IGF on meningioma risk through various methodologies: MR-Egger (OR = 1.16 ,SE = 0.17 ,P = 0.38), Weighted median (OR = 1.14 ,SE = 0.045 ,P = 0.0028), Inverse variance weighted (OR = 1.14 ,SE = 0.035 ,P < 0.00019 ), Simple mode( OR = 1.15 ,SE = 0.089,P < 0.14 ), and Weighted mode( OR=1.14, SE༝0.085, P༝0.12 ). The heterogeneity analysis revealed Cochran’s Q-test derived p-values of 0.98 for MR-Egger and 0.99 for IVW. The directional horizontal pleiotropy test utilizing egger_intercept yielded a p-value of 0.92 (Table 2 ) . Additionally, we observed general evidence supporting a potential causal influence of LH on meningioma risk across multiple methodologies: Weighted median (OR=1.07, SE༝0.012, P<00001), Inverse variance weighted(OR༝1.07, SE༝0.009, P<00001), Simple mode (OR༝1.06, SE༝0.021, P = 0.0029), and Weighted mode (OR = 1.07, SE = 0.019, P = 0.0011). Conversely, the MR-Egger method yielded opposing results(OR = 0.98、SE = 0.034、p = 0.55). Heterogeneity was assessed with Cochran's Q-test resulting in a p-value of 0.98 for MR-Egger and 0.99 for IVW. The test for directional horizontal pleiotropy using egger_intercept showed a p-value of 0.0069 (Table 2 ). We did not observe any evidence supporting a potential causal influence of ER, FSH, GHR, HCG, FSH, PR, Thyroxine on meningioma risk. The details of MR analysis results showed in supply Table 1 . The causal relationships between genetically predicted sex hormones and the risk of meningioma are depicted in the forest plot and scatter plot shown in Figs. 1 and 2 , while detailed information regarding sensitivity analyses is provided in Fig. 3 . Furthermore, the funnel plot demonstrated symmetry, indicating a lack of pleiotropy. (Fig. 4 ) 3.3 Immunohistochemical analysis The immunohistochemical features of sex hormones are shown in Table 3 . The H-score of PRLR in pregnancy group was 120.35 ± 12.46 which was significantly higher than that in without pregnancy group (< 0.001). The H-score of IGF in pregnancy group was 126.00 ± 4.01 which was significantly higher than that in without pregnancy group (< 0.001). The H-score of LH in pregnancy group was 122.31 ± 7.89 which was significantly higher than that in without pregnancy group (< 0.001) (Fig. 5 ). While, there was no significant difference of H-score between two groups in other sex hormones. Table 3 Mendelian randomization (MR) analysis of hormone and meningeoma PRLR Method OR SE P-value Results MR Egger 0.76 0.26 0.29 Weighted median 1.50 0.067 < 0.001 Inverse variance weighted 1.44 0.051 < 0.001 Simple mode 1.50 0.11 0.0011 Weighted mode 1.51 0.11 0.0019 Method Q Q_df Q_pval Heterogeneity test MR Egger 3.46 17 0.99 Inverse variance weighted 10.08 18 0.93 egger_intercept se P-value Test for directional horizontal pleiotropy 0.11 0.04 0.02 IGF Method OR SE P-value Results MR Egger 1.16 0.17 0.38 Weighted median 1.14 0.045 0.0028 Inverse variance weighted 1.14 0.035 0.00019 Simple mode 1.15 0.089 0.14 Weighted mode 1.14 0.085 0.12 Method Q Q_df Q_pval Heterogeneity test MR Egger 0.47 44 0.98 Inverse variance weighted 0.48 45 0.99 egger_intercept se P-value Test for directional horizontal pleiotropy -0.002 0.024 0.92 LH Method OR SE P-value Results MR Egger 0.98 0.034 0.55 Weighted median 1.07 0.012 < 0.001 Inverse variance weighted 1.07 0.0090 < 0.001 Simple mode 1.06 0.021 0.0029 Weighted mode 1.07 0.019 0.0011 Method Q Q_df Q_pval Heterogeneity test MR Egger 8.45 101 0.98 Inverse variance weighted 16.04 101 0.99 egger_intercept se P-value Test for directional horizontal pleiotropy 0.056 0.020 0.0069 4. Discussion In the general population, the prevalence of meningiomas is estimated to be approximately 97.5 per 100,000 individuals[ 14 , 15 ]. Brain tumors in pregnant patients are exceedingly rare, with estimates indicating that meningiomas occur at a frequency of 1 to 4.5 per 100,000 based on data from females aged 15 to 44 years[ 16 , 17 ]. The rapid progression associated with significant enlargement of a meningioma during pregnancy raises considerable concern and supports the rationale for early surgical intervention[ 18 , 19 ]. Previous research has indicated that both exogenous hormonal therapy and increased endogenous levels of estrogen and/or progesterone could influence the likelihood of developing meningioma[ 6 , 20 ]. A cohort study involving women demonstrated an elevated risk for meningiomas associated with estrogen therapy; however, this association was not observed with combined estrogen-progesterone treatment according to Korhonen’s study[ 21 , 22 ]. All of the several evidences indicate that hormones may play a significant role in both the development and progression of meningioma. Yet, to this day, no definitive evidence exists regarding which type, combination, dosage or duration of hormone administration might significantly influence either the onset or advancement of meningiomas. In our study, we found strong evidence of a potential causal effect of PRLR, IGF and LH on the risk of meningioma using the Mendelian randomization analysis. The Immunohistochemical analysis also showed the H-score of PRLR, IGF and LH in pregnancy group was significantly higher than that in without pregnancy group. Consequently, our study also demonstrated that several hormones such as PRLR, IGF and LH would play an important role in the progression of meningioma. Several studies have observed that progesterone and/or estrogen receptors expressed in specific meningiomas[ 6 , 7 , 23 ]. Donnell and colleagues identified both progesterone and estrogen receptors utilizing specific synthetic radioligands in six cases of meningioma[ 24 ]. Similarly, Lesch and Fahlbusch conducted an analysis of 70 cases for the detection of estrogen, progesterone, and androgen receptors through double labeling assays[ 25 , 26 ]. It is noteworthy that many of these investigations are over three decades old, further research on the presence of these receptors in surgical specimens should been proceed. Nevertheless, our study employing Mendelian randomization analysis combined with immunohistochemical methods did not demonstrate a significant role for either progesterone or estrogen in the progression of meningioma. A substantial body of evidence indicates that, in the vitro study, prolactin can promote the proliferation rate of meningioma cells[ 27 , 28 ]. Our study further substantiates that PRL may play a crucial role in the progression of meningiomas. Importantly, prolactin acts both as a circulating hormone and as a paracrine/autocrine factor, participated in angiogenesis—a vital component of tumorigenesis processes, including those related to meningiomas. Therefore, angiogenesis could represent an additional pathway through which prolactin affects the growth rate of meningiomas. Moreover, the primary influence of prolactin on rapidly growing meningiomas during pregnancy may be associated with its critical function in osmoregulation[ 19 , 28 ]. There is increasing evidence demonstrating significant effects exerted by prolactin on osmoregulation across diverse cell types and tissues. The prolactin-responsive cells were mainly activated by mitogen-activated protein kinases (MAPKs), phosphatidylinositol-3-kinase (PI3K), proto-oncogene c-Src, and JAK/STAT signaling pathways[ 29 – 31 ]. Theoretically, osmoregulation could serve as a potential mechanism resulting in rapid changes in meningioma volume similar to those observed during and after pregnancy. The prolactin could modify intracellular and extracellular fluid levels via its impact on various ion channels and transporters[ 32 , 33 ]. At present, these proposed effects necessitate more comprehensive investigation. The research about meningiomas has implicated gonadal steroid hormones as well as receptor expression associated with the growth hormone (GH/IGF-1) signaling pathway[ 34 , 35 ]. Our study further substantiates the hypothesis that IGF may play a crucial role in the progression of meningiomas. Meningiomas are known to express receptors for GH and IGF-1, as demonstrated by various studies[ 36 – 39 ]. Additional investigations have reported elevated levels of expression for both IGF and its receptor in vitro study[ 40 , 41 ]. Puduvalli et al. explored the effects of fenretinide on meningioma cells and identified that fenretinide could induces apoptosis in meningioma cells by inhibiting IGF-1[ 42 ]. While, the existing literature presents conflicting viewpoint regarding the roles of GH and IGF-1 receptors in oncogenesis related to meningiomas[ 34 , 37 ]. Both experimental studies and mouse models have found that blocking of GH or IGF-1 receptors could decelerate the growth rate of meningioma cells[ 43 ]. The researches above suggested that elevated levels of IGF-1 resulting from GH hypersecretion during acromegaly could contribute significantly to the progression of meningiomas. A multitude of studies has demonstrated luteinizing hormone (LH) and LH-releasing hormone (LHRH) play a role in the proliferation of meningiomas[ 44 – 46 ]. A previous case reported that a patient with meningioma underwent subtotal resection and remained recurrence-free for about 18 years and experienced rapid tumor progression after treatment with an LHRH agonist[ 44 , 47 ]. Nevertheless, this accelerated progression occurring alongside leuprorelin (LHRH agonist) administration suggests the potential involvement of alternative hormonal mechanisms. Additionally, another study identified LHR expression in 92% of male meningiomas within a retrospective analysis[ 48 ]. Our investigation further corroborated that LH may significantly influence meningioma progression. However, Mendelian randomization analysis indicated that the causal relationship between LH and the risk of developing meningioma was not robust. Nevertheless, several limitations were noted. First, all data from the GWASs were derived from a European population, and it remains to be established whether our findings are generalizable to other populations. Second, it is important to consider the heterogeneity among patients with meningioma. Hormones may exhibit a causal relationship with specific subtypes of meningiomas. Future investigations should contemplate conducting more extensive studies that encompass various subgroups of meningiomas. 5. Conclusion This is the first MR study to explore the causality from hormone on meningioma. The mendelian randomization analysis and immunohistochemical analysis demonstrated that PRLR, IGF and LH played an important role in the progression of meningioma. The targeted drug for PRLR, IGF and LH may become novel therapeutic methods for meningioma in the future. Abbreviations GWAS Genome-Wide Association Studies MR Mendelian randomization WME weighted median IVW inverse variance weighted HRP polymer-horseradish peroxidase IVs instrumental variables PRLR prolactin receptor ER estrogen receptor FSH follicle-stimulating hormone GHR growth hormone receptor HCG Human Chorionic Gonadotropin IGF Insulin-Like Growth Factor LH Luteinizing Hormone TSH Thyroid Stimulating Hormone PR progesterone receptor EAF allele frequency EA effect allele LD linkage disequilibrium SNPs single nucleotide polymorphisms OR Odds Ratio Declarations Conflict of interest: The authors declare no conflict of interests for this article. Ethics approval and consent to participate The study was approved by the Ethics Committee of Qingdao Hospital, University of Health and Rehabilitation Sciences (Qingdao Municipal Hospital) (Approval No.2025-KY-003). Because the study was retrospective and observational, the board waived the patients’ informed consent. Clinical trial number: not applicable. Consent for publication Not applicable Conflict of interest The authors declare no conflict of interests for this article. Funding This work was supported by the China’s government under grant of National Natural Science Foundation (#82001184). Author Contribution All authors take responsibility for the integrity and the accuracy of this manuscript. Study concept and design: Mingxing Liu, Weimin Wang and Jianan Zhang; Draft of the manuscript: Tong Li, Yongyi Wang, Feng Chen; Acquisition of data: Yong Zhou, Xiaoqun Hou, and Shengli Li; Statistical analysis: Zhiming Xu; Edit: Weimin Wang, Peng Liu. Acknowledgement none Data Availability The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request. References Maggio I, Franceschi E, Tosoni A, Nunno VD, Gatto L, Lodi R, Brandes AA (2021) Meningioma: not always a benign tumor. 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J Steroid Biochem Mol Biol 37:1061–1067. 10.1016/0960-0760(90)90466-x Nicosia L, Bucpapaj R, Barresi V, Damante R, Napoli G, Ghimenton C, Giaj-Levra N, Cancedda M, Flaminio S, Figlia V, Alongi F (2021) Resected atypical meningioma relapsed to anaplastic meningioma during luteinizing hormone-releasing hormone agonist therapy. Neurochirurgie 67:193–197. 10.1016/j.neuchi.2020.10.002 Li Q, Coulson H, Klaassen Z, Sharma S, Ramalingam P, Moses KA, Terris MK (2013) Emerging association between androgen deprivation therapy and male meningioma: significant expression of luteinizing hormone-releasing hormone receptor in male meningioma. Prostate Cancer Prostatic Dis 16:387–390. 10.1038/pcan.2013.45 Anda T, Honda M, Ishihara T, Kamei T (2014) Progression of intracranial meningioma during luteinizing hormone-releasing hormone agonist treatment for prostate cancer: case report. Neurol Med Chir (Tokyo) 54:327–330. 10.2176/nmc.cr2012-0417 Franke AJ, Skelton WI, Woody LE, Bregy A, Shah AH, Vakharia K, Komotar RJ (2018) Role of bevacizumab for treatment-refractory meningiomas: A systematic analysis and literature review. Surg Neurol Int 9:133. 10.4103/sni.sni_264_17 Additional Declarations No competing interests reported. Supplementary Files supplytable.xlsx Cite Share Download PDF Status: Posted Version 1 posted 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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1","display":"","copyAsset":false,"role":"figure","size":83844,"visible":true,"origin":"","legend":"\u003cp\u003eForest plots of causal effects of PRLR (A), IGF (B), LH (C) -associated single nucleotide polymorphisms (SNPs) on meningioma.\u003c/p\u003e","description":"","filename":"Onlinefigure1.png","url":"https://assets-eu.researchsquare.com/files/rs-7564859/v1/2e8f4e4c36f1045068ab1674.png"},{"id":93008967,"identity":"9dbe7f12-c5a4-4915-81b2-aa228f35fccd","added_by":"auto","created_at":"2025-10-08 07:05:52","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":33175,"visible":true,"origin":"","legend":"\u003cp\u003eScatter plots of genetic association between PRLR (A), IGF (B), LH (C) and meningioma.\u003c/p\u003e","description":"","filename":"Onlinefigure2.png","url":"https://assets-eu.researchsquare.com/files/rs-7564859/v1/752120516191e56a92404cb3.png"},{"id":93007025,"identity":"c526bd18-ac6f-48f9-8272-80e786e39e3a","added_by":"auto","created_at":"2025-10-08 06:49:53","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":21872,"visible":true,"origin":"","legend":"\u003cp\u003eLeave one out of sensitivity tests. Calculate the MR results of the remaining IVs after removing the IVs one by one. PRLR (A), IGF (B), LH (C).\u003c/p\u003e","description":"","filename":"Onlinefigure3.png","url":"https://assets-eu.researchsquare.com/files/rs-7564859/v1/fce94bb8fac48f511b114011.png"},{"id":93007013,"identity":"751b19c9-8dae-486a-8070-ddadfeddfa00","added_by":"auto","created_at":"2025-10-08 06:49:52","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":51785,"visible":true,"origin":"","legend":"\u003cp\u003eThe funnel plot demonstrated symmetry, indicating a lack of pleiotropy. PRLR (A), IGF (B), LH (C).\u003c/p\u003e","description":"","filename":"Onlinefigure4.png","url":"https://assets-eu.researchsquare.com/files/rs-7564859/v1/0e584148726b2e1a35a4c6bb.png"},{"id":93007849,"identity":"2c09f199-030f-42be-92e2-02cb34555043","added_by":"auto","created_at":"2025-10-08 06:57:53","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":210703,"visible":true,"origin":"","legend":"\u003cp\u003eThe immunohistochemical features of sex hormones. The H-score of PRLR (A), IGF (B), LH (C) in pregnancy group was significantly higher than that in without pregnancy group (D, E, F) (\u0026lt;0.001).\u003c/p\u003e","description":"","filename":"Onlinefigure5.png","url":"https://assets-eu.researchsquare.com/files/rs-7564859/v1/33f4b00a0f5046a168368798.png"},{"id":93866755,"identity":"a36ff1fb-f306-4a5b-b8b6-d4d55c069b2c","added_by":"auto","created_at":"2025-10-19 09:16:47","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1501226,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7564859/v1/13a78af0-d08e-4552-b7c7-e47fac62ab2e.pdf"},{"id":93007038,"identity":"a30dbcb6-6835-475d-8899-3401ec451e44","added_by":"auto","created_at":"2025-10-08 06:49:54","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":28588,"visible":true,"origin":"","legend":"","description":"","filename":"supplytable.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7564859/v1/7f4176314303522d035ab21e.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Sex hormones and meningiomas risk: a Mendelian randomization and immunohistochemical analysis","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eMeningiomas are common, slow-growing primary intracranial neoplasms, which occur in individuals during their fifth and sixth decades of life, with a notable female predominance (female-to-male ratio of 2:1)[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Previous studies reported cases in which meningiomas progressed and manifested symptoms during pregnancy, experienced remission postpartum[\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. The growth dynamics of meningiomas may be influenced by changes of sex steroid hormones during pregnancy. Previous vitro studies indicate that both progesterone and estrogen can independently stimulate meningioma proliferation[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Several studies have demonstrated that meningioma exhibit immunoreactivity for both (progesterone receptor) PgR and (estrogen receptor) ER receptors[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Therefore, pregnancy may significantly impact the progression of meningioma. Although there is evidence suggesting a link between sex hormones and progression of meningioma, the specific mechanisms underlying tumor growth during pregnancy remain poorly understood. Surgical excision continues to be the standard treatment modality for meningiomas; however, complications arise when addressing recurrent tumors or those resistant to radiation therapy which hinder complete surgical removal efforts[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. In neoplasms cases where hormone receptors are present within the tumor tissue, additional antihormonal targeted therapies could offer significant benefits for affected patients[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eTo further explore the causal relationship between sex hormones and the risk of meningioma, we employed Mendelian randomization (MR) and immunohistochemistry for causal inference analysis. To corroborate our MR findings, we performed an immunohistochemical study involving a cohort consisting of 16 cases of meningioma. Furthermore, we examined differences in immunohistochemistry related to sex hormone receptors alongside clinical characteristics between pregnant women and non-pregnant individuals.\u003c/p\u003e"},{"header":"2. Materials And Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Study Design\u003c/h2\u003e\u003cp\u003eIn order to infer the causal effects of exposures on outcomes, the MR analysis utilizes the random allocation of genetic variants at conception. In our study, the exposures comprised a range of sex hormones, while the outcome was meningioma. The evaluation of causal effects was performed within a univariable two-sample MR framework. As previous studies showed that the instrumental variables (IVs) must satisfy three essential assumptions: (i) IV is associated with exposure; (ii) IV is not associated with confounders that influence both exposure and outcome; and (iii) IV does not have a direct association with the outcome[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Data sources\u003c/h2\u003e\u003cp\u003eWe gathered genetic data from three GWAS databases: Finn Gens database, GWAS Catalog, and IEU Open GWAS, as detailed in Supplementary Table\u0026nbsp;1. We obtained GWAS data relevant to meningioma from FinnGen, which included 314,708 participants (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://storage.googleapis.com/finngen-public-data-r10/summary_stats/finngen_R10_C3_MENINGIOMA_EXALLC.gz\u003c/span\u003e\u003cspan address=\"https://storage.googleapis.com/finngen-public-data-r10/summary_stats/finngen_R10_C3_MENINGIOMA_EXALLC.gz\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Additionally, summaries for PRLR (prolactin receptor), ER (estrogen receptor), FSH (follicle-stimulating hormone), GHR (growth hormone receptor), HCG (Human Chorionic Gonadotropin), IGF (Insulin-Like Growth Factor), LH (Luteinizing Hormone), TSH (Thyroid Stimulating Hormone), and PR (progesterone receptor) were acquired from \"IEU OpenGWAS,\" whereas Thyroxine's GWAS summary was retrieved from the GWAS Catalog (GCST90266075). For all datasets analyzed, we compiled information on SNPs including effect allele frequency (EAF), effect allele (EA), effect size(β), and p-value. No additional ethical approval was required because that all data were publicly accessible.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Selection of instrument variables\u003c/h2\u003e\u003cp\u003eInitially, genetic polymorphisms exhibiting a significant association with meningioma on a genome-wide scale (p\u0026thinsp;\u0026lt;\u0026thinsp;5 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;10\u003c/sup\u003e) were employed as instrumental variables. We established an r-squared threshold of less than 0.0001 to reduce potential bias stemming from linkage disequilibrium (LD) and ensure the independence of these IVs. The single nucleotide polymorphisms (SNPs) that displayed a substantial correlation with exposure factors were identified using the F-statistic for SNPs, where strong IVs were characterized by an F value exceeding 10. Information pertaining to sex hormones and meningioma outcomes was extracted through GWAS to investigate the relationship between SNPs fulfilling Hypotheses 1, 2, and 3 and their corresponding outcomes. Finally, data obtained from both exposure and outcome variable databases were integrated and organized; palindromic sequences were removed to ensure consistency in effector alleles across all SNPs.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Mendelian randomization analysis\u003c/h2\u003e\u003cp\u003eWe conducted a two-sample MR analysis utilizing five analytical techniques: weighted median (WME), inverse variance weighted (IVW), simple mode, MR-Egger regression, and weighted mode. Cochran\u0026rsquo;s Q test was employed to evaluate heterogeneity across individual genetic variance estimates. We implemented a random effects model within IVW for our final MR analysis, if the p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05. A threshold of less than 0.05 for p-values was established as a demarcation point to differentiate statistically significant differences from those deemed statistically insignificant. The results were visualized through scatter plots and forest plots to depict the relationship between sex hormones and meningioma incidence. We also used the forest plots and funnel plots to evaluated the heterogeneity and stability of our results. The results of IVW method were scrutinized using multinomial residuals and outlier detection via the MR-PRESSO technique to ensure robust and precise interpretations of underlying relationships.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Tissue samples\u003c/h2\u003e\u003cp\u003eThis investigation represents a retrospective analysis conducted from January 2010 to October 2024, concentrating on primary intracranial meningiomas diagnosed in the pathology department. Specimens were obtained from four cases of meningioma that either manifested or progressed during pregnancy or the postpartum period, in addition to twelve cases of meningioma unrelated to pregnancy, sourced from Qingdao Municipal Hospital. Clinical data for the patients were retrieved from the computerized hospital information system. Tumor tissue samples fixed in 10% neutral-buffered formalin were sectioned into slices with a thickness of 4\u0026ndash;5 \u0026micro;m. Meningioma grading and histological classification were performed according to the WHO classification established in 2021. Immunohistochemical staining for sex hormone receptors was executed using a polymer-horseradish peroxidase (HRP) method. The study was approved by the Ethics Committee of Qingdao Hospital, University of Health and Rehabilitation Sciences (Qingdao Municipal Hospital) (Approval No.2025-KY-003). Because the study was retrospective and observational, the board waived the patients\u0026rsquo; informed consent.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.6 Immunohistochemistry procedure\u003c/h2\u003e\u003cp\u003eSections of 4 \u0026micro;m thick, formalin-fixed, paraffin-embedded tissue were prepared on slides that had been pre-coated with 3-aminopropyltriethoxysilane. The slides were incubated overnight at a temperature of 37\u0026deg;C. Endogenous peroxidase activity was blocked using 3% hydrogen peroxide (H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e). Following this step, the sections were rinsed in Tris buffer (pH 7.6) and incubated for 30 minutes at room temperature with various antibodies: anti-PRLR (Rabbit polyclonal antibody, Q08501, BOSTER, Wuhan City, China), anti-ER (Rabbit polyclonal antibody, P03372, BOSTER), anti-FSH (Rabbit polyclonal antibody, P23945, BOSTER), anti-GHR (Rabbit polyclonal antibody AB_2840000 from Affinity Biosciences in Jiangsu), anti-HCG (Rabbit polyclonal antibody P03372 from BOSTER), anti-IGF (Rabbit polyclonal antibody P08069 from BOSTER), anti-LH (Rabbit polyclonal antibody P22888 from BOSTER), anti-Thyroxine (Rabbit polyclonal antibody P16473 from ZEN-BIOSCIENCE in Beijing), anti-TSH (Rabbit polyclonal antibody AB_2766360 from Wuhan City), and anti-PR (Rabbit polyclonal antibody P06401 sourced from both BOSTER and ABclonal Biotechnology Co., Ltd., Wuhan City).\u003c/p\u003e\u003cp\u003eThe slides were then treated with polymer-HRP reagent and diaminobenzidine chromogen after being washed again in Tris buffer. Counterstaining was conducted using Harris\u0026rsquo;s hematoxylin followed by dehydration and mounting with dibutyl phthalate xylene.\u003c/p\u003e\u003cp\u003eThree distinct fields of view at a magnification of 100x were captured for each section utilizing scanning equipment: Aperio Versa 8 manufactured by Leica. These images underwent analysis through the Image Scope software module provided by Leica. The software is programmed to classify all deep brown areas on the tissue sections as strongly positive; light brown areas as moderately positive; pale yellow areas as weakly positive; and blue cell nuclei as negative. Subsequently, the software identifies each point within the tissue to calculate the area occupied by strong positives, moderate positives, weak positives, and negatives measured in pixels along with their respective percentages before calculating the histochemistry score known as H-score using the formula: H-SCORE = [(Strong positive percentage) x 3] + [(Medium positive percentage) x 2] + [(Weak positive percentage) x 1].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e2.7 Statistical analysis\u003c/h2\u003e\u003cp\u003eThe age and H-score were reported as percentages along with the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation. The WHO grade was similarly represented in terms of percentages. For group comparisons involving categorical variables, chi-square tests or Fisher\u0026rsquo;s exact tests were utilized, whereas t-tests or Mann-Whitney U tests were employed for continuous variables. A p-value of less than 0.05 was considered statistically significant. Statistical analyses were performed using R version 3.4.3 (R Foundation).\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Demographic characteristics\u003c/h2\u003e\u003cp\u003eThe clinical characteristics are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Our study included 16 cases of meningioma, of which 4 were associated with pregnancy. The mean age of the 16 meningioma patients was 53.56\u0026thinsp;\u0026plusmn;\u0026thinsp;17.92 years. Among these, there were 10 cases (62.5%) classified as grade I and 6 cases (37.5%) classified as grade II. Details regarding basal immunohistochemistry for CK, Ema, Stat6, Ki67, and Sstr2 are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The average age of meningioma patients with pregnancy was significantly younger at 32.50\u0026thinsp;\u0026plusmn;\u0026thinsp;1.29 years compared to those without pregnancy (60.58\u0026thinsp;\u0026plusmn;\u0026thinsp;14.91 years; p\u0026thinsp;=\u0026thinsp;0.002). Additionally, the proportion of grade II cases among those with pregnancy was found to be significantly higher at 100% compared to only 16.7% in those without pregnancy.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCharacteristics of patients of meningioma\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eItems\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTraining cohort\u003c/p\u003e\u003cp\u003e(N\u0026thinsp;=\u0026thinsp;16)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge(years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e53.56\u0026thinsp;\u0026plusmn;\u0026thinsp;17.92\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGrade\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eI\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10(62.5%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eII\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6(37.5%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eIII\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePregnancy(n, %)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eYes\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4(25.0%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eNo\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12(75.0%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIHC ( n, %)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eCK (+)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1(6.25%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eEma (+)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11(68.75%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eVimentin (+)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e16(100.0%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eStat6 (+)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eS100 (+)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4(25.0%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eKi67 (+,%)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.56\u0026thinsp;\u0026plusmn;\u0026thinsp;2.63\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eGfap (+)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4(25.0%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eSstr2 (+)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10(62.5%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Causal effects of sex hormones on meningioma\u003c/h2\u003e\u003cp\u003eWe found substantial evidence suggesting a potential causal relationship between PRLR and the risk of meningioma, as demonstrated by several methods: Weighted median (OR\u0026thinsp;=\u0026thinsp;1.50, SE\u0026thinsp;=\u0026thinsp;0.067, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), Inverse variance weighted (OR\u0026thinsp;=\u0026thinsp;1.44, SE\u0026thinsp;=\u0026thinsp;0.051, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), Simple mode (OR\u0026thinsp;=\u0026thinsp;1.5, SE\u0026thinsp;=\u0026thinsp;0.11, P\u0026thinsp;=\u0026thinsp;0.0011), and Weighted mode (OR\u0026thinsp;=\u0026thinsp;1.51, SE\u0026thinsp;=\u0026thinsp;0.11, P\u0026thinsp;=\u0026thinsp;0.0019). In contrast, the MR-Egger approach produced opposing results (OR\u0026thinsp;=\u0026thinsp;0.76, SE\u0026thinsp;=\u0026thinsp;0.26, p\u0026thinsp;=\u0026thinsp;0.29). Heterogeneity was evaluated using Cochran's Q-test with resulting p-values of 0.99 for MR-Egger and 0.93 for IVW (Table\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eThe immunohistochemical results of Meningioma cases\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eItems\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eMeningioma cases (n\u0026thinsp;=\u0026thinsp;16)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eP\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWithout pregnancy (n\u0026thinsp;=\u0026thinsp;12)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eWith pregnancy (n\u0026thinsp;=\u0026thinsp;4)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge(years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e60.58\u0026thinsp;\u0026plusmn;\u0026thinsp;14.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e32.50\u0026thinsp;\u0026plusmn;\u0026thinsp;1.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGrade\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eI\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10(83.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eII\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2(16.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4(100%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eIII\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIHC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003ePRLR\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e73.28\u0026thinsp;\u0026plusmn;\u0026thinsp;18.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e120.35\u0026thinsp;\u0026plusmn;\u0026thinsp;12.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eER\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e36.06\u0026thinsp;\u0026plusmn;\u0026thinsp;21.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e19.20\u0026thinsp;\u0026plusmn;\u0026thinsp;2.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.147\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eFSH\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e14.87\u0026thinsp;\u0026plusmn;\u0026thinsp;3.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e17.31\u0026thinsp;\u0026plusmn;\u0026thinsp;4.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.247\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eGHR\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11.37\u0026thinsp;\u0026plusmn;\u0026thinsp;1.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14.45\u0026thinsp;\u0026plusmn;\u0026thinsp;3.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.189\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eHCG\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11.36\u0026thinsp;\u0026plusmn;\u0026thinsp;2.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11.38\u0026thinsp;\u0026plusmn;\u0026thinsp;1.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.985\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eIGF\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e63.59\u0026thinsp;\u0026plusmn;\u0026thinsp;6.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e126.00\u0026thinsp;\u0026plusmn;\u0026thinsp;4.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eLH\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e49.81\u0026thinsp;\u0026plusmn;\u0026thinsp;8.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e122.31\u0026thinsp;\u0026plusmn;\u0026thinsp;7.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eThyroxine\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e24.36\u0026thinsp;\u0026plusmn;\u0026thinsp;6.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18.88\u0026thinsp;\u0026plusmn;\u0026thinsp;2.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.107\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eTSH\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8.27\u0026thinsp;\u0026plusmn;\u0026thinsp;1.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.67\u0026thinsp;\u0026plusmn;\u0026thinsp;1.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.363\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003ePR\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e18.98\u0026thinsp;\u0026plusmn;\u0026thinsp;4.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16.66\u0026thinsp;\u0026plusmn;\u0026thinsp;4.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.344\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eFurthermore, we identified compelling evidence indicating a possible causal effect of IGF on meningioma risk through various methodologies: MR-Egger (OR\u0026thinsp;=\u0026thinsp;1.16 ,SE\u0026thinsp;=\u0026thinsp;0.17 ,P\u0026thinsp;=\u0026thinsp;0.38), Weighted median (OR\u0026thinsp;=\u0026thinsp;1.14 ,SE\u0026thinsp;=\u0026thinsp;0.045 ,P\u0026thinsp;=\u0026thinsp;0.0028), Inverse variance weighted (OR\u0026thinsp;=\u0026thinsp;1.14 ,SE\u0026thinsp;=\u0026thinsp;0.035 ,P\u0026thinsp;\u0026lt;\u0026thinsp;0.00019 ), Simple mode( OR\u0026thinsp;=\u0026thinsp;1.15 ,SE\u0026thinsp;=\u0026thinsp;0.089,P\u0026thinsp;\u0026lt;\u0026thinsp;0.14 ), and Weighted mode( OR=1.14, SE༝0.085, P༝0.12 ). The heterogeneity analysis revealed Cochran\u0026rsquo;s Q-test derived p-values of 0.98 for MR-Egger and 0.99 for IVW. The directional horizontal pleiotropy test utilizing egger_intercept yielded a p-value of 0.92 (Table\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) .\u003c/p\u003e\u003cp\u003eAdditionally, we observed general evidence supporting a potential causal influence of LH on meningioma risk across multiple methodologies: Weighted median (OR=1.07, SE༝0.012, P\u0026lt;00001), Inverse variance weighted(OR༝1.07, SE༝0.009, P\u0026lt;00001), Simple mode (OR༝1.06, SE༝0.021, P\u0026thinsp;=\u0026thinsp;0.0029), and Weighted mode (OR\u0026thinsp;=\u0026thinsp;1.07, SE\u0026thinsp;=\u0026thinsp;0.019, P\u0026thinsp;=\u0026thinsp;0.0011). Conversely, the MR-Egger method yielded opposing results(OR\u0026thinsp;=\u0026thinsp;0.98、SE\u0026thinsp;=\u0026thinsp;0.034、p\u0026thinsp;=\u0026thinsp;0.55). Heterogeneity was assessed with Cochran's Q-test resulting in a p-value of 0.98 for MR-Egger and 0.99 for IVW. The test for directional horizontal pleiotropy using egger_intercept showed a p-value of 0.0069 (Table\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eWe did not observe any evidence supporting a potential causal influence of ER, FSH, GHR, HCG, FSH, PR, Thyroxine on meningioma risk. The details of MR analysis results showed in supply Table\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003eThe causal relationships between genetically predicted sex hormones and the risk of meningioma are depicted in the forest plot and scatter plot shown in Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, while detailed information regarding sensitivity analyses is provided in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Furthermore, the funnel plot demonstrated symmetry, indicating a lack of pleiotropy. (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e)\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Immunohistochemical analysis\u003c/h2\u003e\u003cp\u003eThe immunohistochemical features of sex hormones are shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The H-score of PRLR in pregnancy group was 120.35\u0026thinsp;\u0026plusmn;\u0026thinsp;12.46 which was significantly higher than that in without pregnancy group (\u0026lt;\u0026thinsp;0.001). The H-score of IGF in pregnancy group was 126.00\u0026thinsp;\u0026plusmn;\u0026thinsp;4.01 which was significantly higher than that in without pregnancy group (\u0026lt;\u0026thinsp;0.001). The H-score of LH in pregnancy group was 122.31\u0026thinsp;\u0026plusmn;\u0026thinsp;7.89 which was significantly higher than that in without pregnancy group (\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). While, there was no significant difference of H-score between two groups in other sex hormones.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMendelian randomization (MR) analysis of hormone and meningeoma\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePRLR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMethod\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eOR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSE\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eResults\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMR Egger\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.29\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWeighted median\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.067\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eInverse variance weighted\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.051\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSimple mode\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0011\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWeighted mode\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0019\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eMethod\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eQ\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003eQ_df\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003eQ_pval\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHeterogeneity test\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMR Egger\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.99\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eInverse variance weighted\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.93\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eegger_intercept\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003ese\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003eP-value\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTest for directional horizontal pleiotropy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIGF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eMethod\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eOR\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003eSE\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003eP-value\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eResults\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMR Egger\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.38\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWeighted median\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.045\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0028\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eInverse variance weighted\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.035\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.00019\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSimple mode\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.089\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.14\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWeighted mode\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.085\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eMethod\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eQ\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003eQ_df\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003eQ_pval\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHeterogeneity test\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMR Egger\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.98\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eInverse variance weighted\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.99\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eegger_intercept\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003ese\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003eP-value\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTest for directional horizontal pleiotropy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.024\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eMethod\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eOR\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003eSE\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003eP-value\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eResults\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMR Egger\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.034\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.55\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWeighted median\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.012\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eInverse variance weighted\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0090\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSimple mode\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0029\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWeighted mode\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.019\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0011\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eMethod\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eQ\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003eQ_df\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003eQ_pval\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHeterogeneity test\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMR Egger\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e101\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.98\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eInverse variance weighted\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e101\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.99\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eegger_intercept\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003ese\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003eP-value\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTest for directional horizontal pleiotropy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.056\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.020\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0069\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eIn the general population, the prevalence of meningiomas is estimated to be approximately 97.5 per 100,000 individuals[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Brain tumors in pregnant patients are exceedingly rare, with estimates indicating that meningiomas occur at a frequency of 1 to 4.5 per 100,000 based on data from females aged 15 to 44 years[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. The rapid progression associated with significant enlargement of a meningioma during pregnancy raises considerable concern and supports the rationale for early surgical intervention[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Previous research has indicated that both exogenous hormonal therapy and increased endogenous levels of estrogen and/or progesterone could influence the likelihood of developing meningioma[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. A cohort study involving women demonstrated an elevated risk for meningiomas associated with estrogen therapy; however, this association was not observed with combined estrogen-progesterone treatment according to Korhonen\u0026rsquo;s study[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. All of the several evidences indicate that hormones may play a significant role in both the development and progression of meningioma. Yet, to this day, no definitive evidence exists regarding which type, combination, dosage or duration of hormone administration might significantly influence either the onset or advancement of meningiomas.\u003c/p\u003e\u003cp\u003eIn our study, we found strong evidence of a potential causal effect of PRLR, IGF and LH on the risk of meningioma using the Mendelian randomization analysis. The Immunohistochemical analysis also showed the H-score of PRLR, IGF and LH in pregnancy group was significantly higher than that in without pregnancy group. Consequently, our study also demonstrated that several hormones such as PRLR, IGF and LH would play an important role in the progression of meningioma.\u003c/p\u003e\u003cp\u003eSeveral studies have observed that progesterone and/or estrogen receptors expressed in specific meningiomas[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Donnell and colleagues identified both progesterone and estrogen receptors utilizing specific synthetic radioligands in six cases of meningioma[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Similarly, Lesch and Fahlbusch conducted an analysis of 70 cases for the detection of estrogen, progesterone, and androgen receptors through double labeling assays[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. It is noteworthy that many of these investigations are over three decades old, further research on the presence of these receptors in surgical specimens should been proceed. Nevertheless, our study employing Mendelian randomization analysis combined with immunohistochemical methods did not demonstrate a significant role for either progesterone or estrogen in the progression of meningioma.\u003c/p\u003e\u003cp\u003eA substantial body of evidence indicates that, in the vitro study, prolactin can promote the proliferation rate of meningioma cells[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Our study further substantiates that PRL may play a crucial role in the progression of meningiomas. Importantly, prolactin acts both as a circulating hormone and as a paracrine/autocrine factor, participated in angiogenesis\u0026mdash;a vital component of tumorigenesis processes, including those related to meningiomas. Therefore, angiogenesis could represent an additional pathway through which prolactin affects the growth rate of meningiomas. Moreover, the primary influence of prolactin on rapidly growing meningiomas during pregnancy may be associated with its critical function in osmoregulation[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. There is increasing evidence demonstrating significant effects exerted by prolactin on osmoregulation across diverse cell types and tissues. The prolactin-responsive cells were mainly activated by mitogen-activated protein kinases (MAPKs), phosphatidylinositol-3-kinase (PI3K), proto-oncogene c-Src, and JAK/STAT signaling pathways[\u003cspan additionalcitationids=\"CR30\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Theoretically, osmoregulation could serve as a potential mechanism resulting in rapid changes in meningioma volume similar to those observed during and after pregnancy. The prolactin could modify intracellular and extracellular fluid levels via its impact on various ion channels and transporters[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. At present, these proposed effects necessitate more comprehensive investigation.\u003c/p\u003e\u003cp\u003eThe research about meningiomas has implicated gonadal steroid hormones as well as receptor expression associated with the growth hormone (GH/IGF-1) signaling pathway[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Our study further substantiates the hypothesis that IGF may play a crucial role in the progression of meningiomas. Meningiomas are known to express receptors for GH and IGF-1, as demonstrated by various studies[\u003cspan additionalcitationids=\"CR37 CR38\" citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Additional investigations have reported elevated levels of expression for both IGF and its receptor in vitro study[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Puduvalli et al. explored the effects of fenretinide on meningioma cells and identified that fenretinide could induces apoptosis in meningioma cells by inhibiting IGF-1[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. While, the existing literature presents conflicting viewpoint regarding the roles of GH and IGF-1 receptors in oncogenesis related to meningiomas[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Both experimental studies and mouse models have found that blocking of GH or IGF-1 receptors could decelerate the growth rate of meningioma cells[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. The researches above suggested that elevated levels of IGF-1 resulting from GH hypersecretion during acromegaly could contribute significantly to the progression of meningiomas.\u003c/p\u003e\u003cp\u003eA multitude of studies has demonstrated luteinizing hormone (LH) and LH-releasing hormone (LHRH) play a role in the proliferation of meningiomas[\u003cspan additionalcitationids=\"CR45\" citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. A previous case reported that a patient with meningioma underwent subtotal resection and remained recurrence-free for about 18 years and experienced rapid tumor progression after treatment with an LHRH agonist[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Nevertheless, this accelerated progression occurring alongside leuprorelin (LHRH agonist) administration suggests the potential involvement of alternative hormonal mechanisms. Additionally, another study identified LHR expression in 92% of male meningiomas within a retrospective analysis[\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Our investigation further corroborated that LH may significantly influence meningioma progression. However, Mendelian randomization analysis indicated that the causal relationship between LH and the risk of developing meningioma was not robust.\u003c/p\u003e\u003cp\u003eNevertheless, several limitations were noted. First, all data from the GWASs were derived from a European population, and it remains to be established whether our findings are generalizable to other populations. Second, it is important to consider the heterogeneity among patients with meningioma. Hormones may exhibit a causal relationship with specific subtypes of meningiomas. Future investigations should contemplate conducting more extensive studies that encompass various subgroups of meningiomas.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis is the first MR study to explore the causality from hormone on meningioma. The mendelian randomization analysis and immunohistochemical analysis demonstrated that PRLR, IGF and LH played an important role in the progression of meningioma. The targeted drug for PRLR, IGF and LH may become novel therapeutic methods for meningioma in the future.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eGWAS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eGenome-Wide Association Studies\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eMR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eMendelian randomization\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eWME\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eweighted median\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eIVW\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003einverse variance weighted\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eHRP\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003epolymer-horseradish peroxidase\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eIVs\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003einstrumental variables\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePRLR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eprolactin receptor\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eER\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eestrogen receptor\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eFSH\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003efollicle-stimulating hormone\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eGHR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003egrowth hormone receptor\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eHCG\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eHuman Chorionic Gonadotropin\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eIGF\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eInsulin-Like Growth Factor\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eLH\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eLuteinizing Hormone\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eTSH\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eThyroid Stimulating Hormone\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eprogesterone receptor\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eEAF\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eallele frequency\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eEA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eeffect allele\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eLD\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003elinkage disequilibrium\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eSNPs\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003esingle nucleotide polymorphisms\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eOR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eOdds Ratio\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eConflict of interest:\u003c/h2\u003e\u003cp\u003eThe authors declare no conflict of interests for this article.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003cp\u003eThe study was approved by the Ethics Committee of Qingdao Hospital, University of Health and Rehabilitation Sciences (Qingdao Municipal Hospital) (Approval No.2025-KY-003). Because the study was retrospective and observational, the board waived the patients\u0026rsquo; informed consent. Clinical trial number: not applicable.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003cp\u003eNot applicable\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003cp\u003eThe authors declare no conflict of interests for this article.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eThis work was supported by the China\u0026rsquo;s government under grant of \u003cem\u003eNational Natural Science Foundation (#82001184).\u003c/em\u003e\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAll authors take responsibility for the integrity and the accuracy of this manuscript. Study concept and design: Mingxing Liu, Weimin Wang and Jianan Zhang; Draft of the manuscript: Tong Li, Yongyi Wang, Feng Chen; Acquisition of data: Yong Zhou, Xiaoqun Hou, and Shengli Li; Statistical analysis: Zhiming Xu; Edit: Weimin Wang, Peng Liu.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003enone\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMaggio I, Franceschi E, Tosoni A, Nunno VD, Gatto L, Lodi R, Brandes AA (2021) Meningioma: not always a benign tumor. A review of advances in the treatment of meningiomas. 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Surg Neurol Int 9:133. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.4103/sni.sni_264_17\u003c/span\u003e\u003cspan address=\"10.4103/sni.sni_264_17\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"meningioma, sex hormones, Mendelian randomization, immunohistochemistry","lastPublishedDoi":"10.21203/rs.3.rs-7564859/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7564859/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eThe sex hormones may induce the progression of meningioma during pregnancy. To further explore the causal relationship between sex hormones and the risk of meningioma, we employed Mendelian randomization (MR) and immunohistochemistry for causal inference analysis.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eWe obtained GWAS data relevant to meningioma from FinnGen, which included 314,708 participants. We found substantial evidence suggesting a potential causal relationship between PRLR, IGF, LH and the risk of meningioma using MR method.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eWe included 16 cases of meningioma, of which 4 were associated with pregnancy. The average age of meningioma patients with pregnancy was significantly younger at 32.50\u0026thinsp;\u0026plusmn;\u0026thinsp;1.29 years compared to those without pregnancy (60.58\u0026thinsp;\u0026plusmn;\u0026thinsp;14.91 years; p\u0026thinsp;=\u0026thinsp;0.002). Additionally, the proportion of grade II cases among those with pregnancy was found to be significantly higher at 100% compared to only 16.7% in those without pregnancy. The H-score of PRLR, IGF and LH in pregnancy group was significantly higher than that in without pregnancy group (\u0026lt;\u0026thinsp;0.001). While, there was no significant difference of H-score between two groups in other sex hormones.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eThe mendelian randomization analysis and immunohistochemical analysis demonstrated that PRLR, IGF and LH played an important role in the progression of meningioma. The targeted drug for PRLR, IGF and LH may become novel therapeutic methods for meningioma in the future.\u003c/p\u003e","manuscriptTitle":"Sex hormones and meningiomas risk: a Mendelian randomization and immunohistochemical analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-08 06:49:47","doi":"10.21203/rs.3.rs-7564859/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8629ffd5-6a7b-4577-9197-4a5f47814a75","owner":[],"postedDate":"October 8th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-19T09:08:40+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-08 06:49:47","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7564859","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7564859","identity":"rs-7564859","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}