Causal Associations Between Liver Function Biomarkers and Prostate Cancer Risk in European and East Asian Populations: A Univariate, Multivariate, and Bidirectional Mendelian Randomization Study

Causal Associations Between Liver Function Biomarkers and Prostate Cancer Risk in European and East Asian Populations: A Univariate, Multivariate, and Bidirectional Mendelian Randomization Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Causal Associations Between Liver Function Biomarkers and Prostate Cancer Risk in European and East Asian Populations: A Univariate, Multivariate, and Bidirectional Mendelian Randomization Study Xinyu Xu, Wenjing Zhu, Yu Peng This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5396719/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Background To investigate the causal relationship between liver function biomarkers and prostate cancer (PCa) in European and East Asian populations using various forms of Mendelian Randomization (MR) and statistical analyses. Methods Single nucleotide polymorphism (SNP), which is strongly associated with exposure factors, was selected as an instrumental variable from genome-wide association studies (GWAS). Inverse variance weighting (IVW), weighted median (WM), MR-Egger, weighted mode, and simple mode were used to investigate the causal relationship between exposure and outcome, and sensitivity analyses were performed to verify the robustness of the results. Results Univariate MR analysis suggested a causal relationship between ALT (OR:0.85, 95% CI(0.75,0.95), P:0.005) and AST (OR:0.90, 95% CI(0.81,1.00), P:0.045) and a reduced risk of PCa in European populations, and a causal relationship between total bilirubin (OR:0.94, 95% CI(0.88,1.00), P:0.049) and direct bilirubin (OR:0.91, 95% CI(0.84,0.99), P:0.022) were causally associated with reduced PCa risk in the East Asian population. The association between total bilirubin (OR:0.74, 95% CI(0.55,0.99), P:0.044) and PCa remained significant after multivariate MR analysis adjusting for confounders. In the reverse MR analysis, a causal relationship between PCa and reduced ALT (OR:0.93, 95% CI(0.88,0.98), P:0.007) was found only in the East Asian population. Sensitivity analyses did not reveal heterogeneity or horizontal pleiotropy. Conclusion There are differences in the causal relationship between liver function biomarkers and PCa in European and East Asian populations. ALT and AST are protective factors for PCa in European populations, and total bilirubin and direct bilirubin in East Asian populations. PCa decreases ALT levels in East Asian populations, which may be one of the characteristic manifestations of PCa paraneoplastic syndrome. Overall, these findings provide ideas for clinical prevention, monitoring and treatment of PCa. prostate cancer liver function biomarkers Mendelian randomization causality Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Prostate cancer (PCa) is a common cancer worldwide. 1.41 million new cases of PCa and 380,000 deaths occurred worldwide in 2020, ranking third and eighth in terms of incidence and mortality of cancers, seriously jeopardizing human health and safety worldwide [ 1 ]. PCa incidence is anomalous between ancestries due to differences in genetic ancestry. The higher risk of PCa in men of African ancestry compared to men of European and Asian ancestry [ 2 ], and the higher incidence of PCa in African Americans (AAs) compared to European Americans (EAs) suggests that genetic factors are involved in the development of PCa [ 3 ]. In Europe and the United States, PCa is the most common malignant tumor in men.2021 American Cancer Society statistics show that PCa accounts for about 27% of the number of men with tumor prevalence [ 4 ]. Although the incidence of PCa in Asian countries is lower than that in Europe and the United States, it has also shown a rapid rising trend in recent years [ 5 ]. As PCa has no obvious symptoms in the early stage, most patients are in the middle or late stage when they are diagnosed. The 5-year survival rate of Chinese PCa patients is only 66.4% [ 6 ], and the 5-year survival rate of PCa patients with distant metastases is only 31% [ 7 ]. Currently, the most common treatments for PCa include active monitoring of prostate-specific antigen (PSA) as well as chemotherapy, radiotherapy, hormonal and surgical treatments. However, PSA has a high rate of false positives and unsupported results, while these tools have limited efficacy in recurrent, drug-resistant and metastatic PCa [ 8 , 9 ]. Abnormal liver function may affect the pathophysiology of PCa, but there is some controversy. It was noted that non-alcoholic fatty liver disease (NAFLD) was an independent risk factor for biochemical recurrence in patients with high-grade metastatic PCa, with a 2-year biochemical recurrence rate of 84.0% and 72.2% in patients with and without NAFLD with a Gleason score of ≥ 4 + 3, and a median biochemical recurrence-free survival of 17 months and 21 months, respectively [ 10 ]. In contrast, another study concluded that NAFLD is protective against biochemical recurrence after radical PCa surgery [ 11 ]. In addition, higher liver fibrosis scores were associated with lower PCa incidence in black men and not in white men [ 12 ]. Men with higher liver fibrosis scores typically have lower PSA levels, and thus men with abnormal liver function have the potential for delayed PCa surveillance [ 13 ]. Liver function markers reflect hepatocyte integrity, cholestasis, and hepatic synthetic function, and include alanine aminotransferase (ALT), aspartate aminotransferase (AST), alkaline phosphatase (ALP), gamma-glutamyl transpeptidase (GGT), and bilirubin. Abnormal liver function is usually characterized by elevated or decreased levels of these markers. Studies have shown that ALT, AST, and ALP levels are significantly elevated in patients with PCa compared to healthy controls, and that liver enzyme concentrations have the potential to be used as a surrogate biomarkers for PCa diagnosis [ 14 ]. A different view was presented by another study, who suggested that the De Ritis ratio (AST/ALT) increased the incidence of PCa, but there was no correlation between AST and ALT and the risk of developing PCa [ 15 ]. We believe that observational studies may be confounded by confounding factors and reverse causality, resulting in heterogeneity of findings. Therefore, more advanced research methods are needed to confirm the relationship between liver function biomarkers and PCa. Mendelian randomization (MR) is an analytical method for assessing the causal relationship between exposure factors and outcomes. The core principle is based on Mendel's second law of inheritance, which ensures that gene-disease associations are not interfered with by confounding factors such as postnatal environment and behaviors, and that the order of causality is reasonable, which can make up for the shortcomings of observational studies [ 16 ], and provides a new method for assessing the relationship between liver function biomarkers and PCa. In this study, we used multiple MR analyses to assess the causal relationship between liver function biomarkers and PCa in a more comprehensive manner and compared the differences between European and East Asian populations, thus providing some evidence for the prevention and monitoring of the course of PCa. Materials and Methods Data sources In this study, several liver function biomarkers were selected as exposure and PCa as outcome, and European and Asian populations were differentiated. Six liver function biomarkers were selected as exposures in the European population, including alanine aminotransferase (ALT), alanine oxaloacetate aminotransferase (AST), glutamylglutamyltransferase (GGT), alkaline phosphatase (ALP), total bilirubin, and direct bilirubin, all of which were derived from the data of a genome-wide association study published by Neale lab. Pooled data on PCa in the European population were published by the Finnish database R11 ( https://www.finngen.fi/ ) and included 17,258 cases and 143,624 controls. Detailed data on exposure and outcome for the European population can be obtained from Table 1 . Seven liver function biomarkers were selected as exposures for the East Asian population, including ALT, AST, GGT, ALP, total bilirubin, direct bilirubin, and indirect bilirubin, which were all published by the Pan-UKB team. GWAS data for PCa in the East Asian population were published by Ishigaki K et al. and included 5,408 cases and 103,939 controls. Detailed data on exposure and outcomes in the East Asian population can be obtained from Table 2 . The data for this study were obtained from publicly available databases and therefore did not require ethical approval or informed consent. Table 1 Information on Population Exposure and Outcome Data in Europe Category Traits Number of SNPs Year Population GWASID Source Exposure Alanine aminotransferase 13,586,000 2018 European ukb-d-30620_irnt https://gwas.mrcieu.ac.uk/ Aspartate aminotransferase 13,586,009 2018 European ukb-d-30650_irnt Gamma glutamyltransferase 13,586,026 2018 European ukb-d-30730_irnt Alkaline phosphatase 13,586,006 2018 European ukb-d-30610_irnt Total bilirubin 13,585,986 2018 European ukb-d-30840_irnt Direct bilirubin 13,584,679 2018 European ukb-d-30660_irnt Outcome Prostate cancer 20,429,334 2024 European FinnGen R11 https://www.finngen.fi/ Table 2 Information on exposure and outcome data for East Asian populations Category Traits ncase ncontrol Year Population GWASID Source Exposure Alanine aminotransferase 2,570 NA 2020 East Asian ukb-e-30620_EAS https://gwas.mrcieu.ac.uk/ Aspartate aminotransferase 2,551 NA 2020 East Asian ukb-e-30650_EAS Gamma glutamyltransferase 2,569 NA 2020 East Asian ukb-e-30730_EAS Alkaline phosphatase 2,572 NA 2020 East Asian ukb-e-30610_EAS Total bilirubin 2,549 NA 2020 East Asian ukb-e-30840_EAS Direct bilirubin 2,159 NA 2020 East Asian ukb-e-30660_EAS Indirect bilirubin 2,165 NA 2020 East Asian ukb-e-recode1_EAS Outcome Prostate cancer 5,408 103,939 2019 East Asian bbj-a-148 Study design In this study, we used univariate, multivariate and bidirectional MR analyses to fully assess the causal relationship between liver function biomarkers and PCa. In the first step, we used univariate MR to analyze the causal relationship between liver function biomarkers and PCa using liver function biomarkers as exposures and PCa as outcomes, and labeled the liver function biomarkers that were causally related to PCa; in the second step, based on the labeled liver function biomarkers, we selected appropriate multiple exposures for multivariate MR analysis to determine whether these liver function biomarkers could serve as an independent influencing factor for PCa; In the third step, using PCa as the exposure factor and all liver function biomarkers as the outcome, reverse MR was used to analyze whether PCa leads to changes in liver function biomarkers, thus determining whether there is a reverse causal association. This study followed three main hypotheses: 1) Association hypothesis. SNP as an instrumental variable must be strongly correlated with exposure factors; 2) Independence assumption. SNP cannot be interfered with by other confounding factors that can affect exposure and outcome; 3) Exclusivity assumption. SNP affects outcome only through exposure factors and not through other pathways. The idea of this study is shown in Fig. 1 . Selection of instrumental variables SNPs obtained from GWAS data were used as instrumental variables in this study. First, to ensure that the included SNPs had strong correlation with liver function biomarkers (exposure factors), we set a series of screening criteria. We set P ≤ 5×10 − 8 in GWAS data for liver function biomarkers in European populations for SNP screening. Because only 1 SNP was obtained for some exposure factors, such as AST, when P ≤ 5×10 − 8 was used in the East Asian population, we set P ≤ 5×10 − 6 in the GWAS data for liver function biomarkers in the East Asian population for SNP screening. Second, in order to ensure that SNPs are not interfered by confounding factors, we performed the process of removing linkage disequilibrium by setting the linkage disequilibrium coefficient r2 = 0.001, and the width of linkage disequilibrium region is 10,000 kb.Third, the echo SNPs with unclear identity of the effector allele were removed.Lastly, in order to avoid the bias caused by weak instrumental variables, we used the F statistic to measure instrumental variables' strength, and F>10 was considered to be free of bias from weak instrumental variables [ 17 ].The formula for F is shown below with the relevant variables labeled: $$\:\text{F}＝{{\beta\:}}^{2}/{SE}^{2}$$ β is the effect value of the allele and SE is the standard error. In the reverse MR analysis, we used PCa as an exposure factor and liver function index as an outcome to explore the reverse causality between them. The screening conditions were consistent with the forward MR analysis, i.e., P ≤ 5×10 − 8 , r2 = 0.001, and kb = 10,000 for the European population, and P ≤ 5×10 − 6 , r2 = 0.001, and kb = 10,000 for the East Asian population.In addition, the screening conditions for the multivariate MR analysis were also consistent with the forward MR analysis. MR analysis Univariate, multivariate, and reverse MR were all analyzed using the inverse variance weighted (IVW) method as the main analytical method, and the IVW method is the most widely used in MR analyses, where the Wald values for each SNP are combined to produce causal estimates for each risk when all instrumental variables are valid [ 18 ]. When there is no heterogeneity in the results, a fixed-effects model is used. Conversely a random effects model was used. However, IVW requires that all instrumental variables must be valid, and IVW estimates may be biased when the included SNPs may be invalid instrumental variables. Therefore, in univariate and reverse MR, we added the weighted median (WM) method as well as MR-Egger regression, weighted mode, and simple mode to increase the robustness of the results. WM is robust to anomalous SNPs because it uses the weighted median of the effect sizes. When at least 50% of the instrumental variables are valid, WM provides robust estimates, reducing potential bias due to a single weak or invalid instrumental variable [ 19 ]. MR-Egger regression detects and accommodates multinomiality of instrumental variables and presents the results through a p-value, with P<0.05 indicating the presence of multinomiality [ 20 ], which at this point provides a less biased effect than IVW estimation [ 21 ]. Weighted models are used to assess the overall causal effect of a large number of genetic instruments, producing lower Type I error rates, less bias, and less statistical power than IVW methods. The simple model calculates the relationship between individual IVs and outcomes, providing intuitive and easy causal inference [ 22 ]. In multivariate MR analyses, the Least Absolute Shrinkage and Selection Operator (LASSO) is used in addition to MR-Egger, which is able to perform variable selection and regularization, which helps to identify the variables that have a significant effect on the outcome variable when there is a large number of potential predictor variables, and improves the accuracy and interpretability of model predictions [ 23 ]. Sensitivity analysis MR-Egger and MR pleiotropy residual sum and outlier (MR pleiotropy residual sum and outlier, MR-PRESSO) were used together to test the multivariate validity of the instrumental variables, and MR-PRESSO's global test was used to detect the horizontal multivariate validity of instrumental variables, with P>0.05 representing there is no polyvalence. When the global test suggests polytropy, the distortion test in MR-PRESSO detects outliers and calculates whether there is a difference in the results before and after removing the outliers, thus reducing the effect of outliers on the statistical results [ 24 ]. Heterogeneity of instrumental variables was assessed by Cochran's Q statistic, with P<0.05 indicating the presence of heterogeneity [ 25 ]. Leave-one-out sensitivity analyses were used to assess whether the presence of bias due to a single SNP affected the overall causal effect. Odds ratios (OR) and 95% confidence intervals (CI) were used to estimate the relative risk triggered by the presence of exposure, and differences were considered statistically significant at P<0.05. All statistics were processed using R4.3.2 TwoSampleMR calculations with a test level of α = 0.05. Results Results of univariate MR analysis After rigorous screening, we obtained SNPs with strong correlation with liver function biomarkers in the European population and calculated the F value. Among them, 179 SNPs were strongly correlated with ALT, with F values ranging from 29.80 to 1025.34. 212 SNPs were strongly correlated with AST, with F values ranging from 29.77 to 3608.30. 257 SNPs were strongly correlated with GGT, with F values ranging from 29.73 to 2463.02. 275 SNPs were strongly correlated with ALP, with F values ranging from 29.75 to 1362.05. 115 SNPs were strongly correlated with ALP, with F values ranging from 29.75 to 1362.05. 115 SNPs were strongly correlated with total bilirubin, with F values ranging from 29.80 to 15029.21. 62 SNPs were strongly correlated with direct bilirubin, with F values ranging from 29.76 to 31283.91. All SNPs showed sufficient statistical efficacy (F>10) to indicate the absence of bias from a weak instrumental variable. SNP details can be obtained from the Supplementary Table 1. In the univariate MR analysis, we used the statistical results of IVW as the main basis. The results showed a causal relationship between ALT (OR:0.85, 95% CI(0.75,0.95), P:0.005) and AST (OR:0.90, 95% CI(0.81,1.00), P:0.045) and a reduced risk of PCa, whereas GGT, ALP, total bilirubin, and direct bilirubin did not have a causal relationship (P>0.05), and the results of other method statistics were not exactly the same as IVW. However, IVW was used as the primary method in our study, so the estimation of the causal effect was reliable. The statistical results of univariate MR analysis are shown in Fig. 2 . Figure 3 demonstrates the scatterplot, funnel plot, and leave-one-out sensitivity analysis plot of liver function biomarkers to PCa. In the sensitivity analysis, heterogeneity of the instrumental variables was calculated by using Cochran's Q. Therefore, the random-effects model of IVW was used for the analysis. MR-Egger and MR-PRESSO both suggested that there was no horizontal multinomiality in the instrumental variables (P>0.05). The results of the sensitivity analyses are presented in Table 3 . The leave-one-out sensitivity analysis did not find any SNPs significantly affecting the results (Fig. 3 ), indicating robust results. Among the liver function biomarkers in East Asian population, 9 SNPs were strongly correlated with ALT after screening, with F-values ranging from 20.97 to 24.90. 2 SNPs were strongly correlated with AST, with F-values ranging from 21.00 to 22.92. 10 SNPs were strongly correlated with GGT, with F-values ranging from 20.86 to 79.79. 4 SNPs were strongly correlated with ALP, with F-values ranging from 21.09 to 80.67. 8 SNPs were strongly correlated with total bilirubin, with F values ranging from 21.35 to 292.08. 7 SNPs were strongly correlated with direct bilirubin, with F values ranging from 22.21 to 183.58. 5 SNPs were strongly correlated with indirect bilirubin, with F values ranging from 20.90 to 229.42. All the SNPs demonstrated sufficient statistical efficacy (F>10), and there were no weak instrumental variables. SNP details are available from Supplementary Table 2. As shown in Fig. 2 , univariate MR analysis showed that there was a causal relationship between total bilirubin (OR:0.94, 95% CI(0.88,1.00), P:0.049) and direct bilirubin (OR:0.91, 95% CI(0.84,0.99), P:0.022) and the reduced risk of PCa, and that there was no causal relationship between ALT, AST, GGT, ALP and indirect bilirubin were not causally associated with the risk of PCa (P>0.05). Figure 4 demonstrates scatterplots, funnel plots, and leave-one-out sensitivity analysis plots of liver function biomarkers with PCa. The sensitivity analyses did not reveal the presence of heterogeneity or horizontal pleiotropy (Table 3 ). (A: scatter plot of ALT to PCa; B: funnel plot of ALT to PCa; C: leave-one-out sensitivity analysis plot of ALT to PCa; D: scatter plot of AST to PCa; E: funnel plot of AST to PCa; F: leave-one-out sensitivity analysis plot of AST to PCa) (A: scatter plot of total bilirubin to PCa; B: funnel plot of total bilirubin to PCa; C: leave-one-out sensitivity analysis plot of total bilirubin to PCa; D: scatter plot of direct bilirubin to PCa; E: funnel plot of direct bilirubin to PCa; F: leave-one-out sensitivity analysis plot of direct bilirubin to PCa) Table 3 Results of sensitivity analysis for univariate and reverse MR Population Exposure Outcome Heterogeneity Pleiotropy IVW_Q Q_pval MR-Egger MR-PRESSO European Alanine aminotransferase Prostate cancer 301.231 2.213E-08 0.487 0.675 Aspartate aminotransferase 408.916 8.845E-05 0.712 0.850 Gamma glutamyltransferase 576.183 1.036E-26 0.253 0.877 Alkaline phosphatase 527.401 3.129E-18 0.910 0.523 Total bilirubin 249.280 4.253E-12 0.323 0.943 Direct bilirubin 115.074 3.509E-05 0.963 0.883 Prostate cancer Alanine aminotransferase 163.288 8.252E-10 0.089 0.537 Aspartate aminotransferase 128.840 1.204E-05 0.629 0.800 Gamma glutamyltransferase 233.836 4.641E-20 0.303 0.400 Alkaline phosphatase 258.345 5.723E-24 0.606 0.900 Total bilirubin 139.519 7.459E-07 0.368 0.100 Direct bilirubin 130.113 8.735E-06 0.902 0.220 East Asian Alanine aminotransferase Prostate cancer 14.293 0.074 0.806 0.117 Aspartate aminotransferase 0.667 0.414 NA NA Gamma glutamyltransferase 13.874 0.127 0.674 0.097 Alkaline phosphatase 7.885 0.048 0.487 0.215 Total bilirubin 4.687 0.698 0.405 0.767 Direct bilirubin 4.000 0.677 0.808 0.770 Indirect bilirubin 5.239 0.264 0.533 0.367 Prostate cancer Alanine aminotransferase 38.729 0.615 0.281 0.587 Aspartate aminotransferase 27.665 0.957 0.996 0.963 Gamma glutamyltransferase 46.942 0.277 0.475 0.320 Alkaline phosphatase 39.039 0.602 0.045 0.615 Total bilirubin 20.038 0.998 0.818 1.000 Direct bilirubin 23.112 0.992 0.723 1.000 Indirect bilirubin 21.211 0.997 0.217 0.997 Results of multivariate MR analysis In multivariate MR analysis, we minimized the use of exposure factors in order to avoid the problem of covariance caused by multiple exposures. In the European population, we chose ALT, AST and GGT as exposure factors and PCa as outcome for multivariate MR analysis. According to the IVW statistics, the protective effect of ALT (OR:0.18, 95% CI(0.71,1.07), P:0.183) and AST (OR:0.83, 95% CI(0.84,1.15), P:0.827) on PCa was no longer significant after the other two biomarkers of liver function were also considered. This finding suggests that the protective effect of ALT and AST on PCa may not be independent of other biomarkers of liver health. That is, they are not independent protective factors for PCa. The heterogeneity test suggested that the instrumental variables were not heterogeneous. Also due to the covariance issue, in the East Asian population, we chose total bilirubin, direct bilirubin and indirect bilirubin as the exposure factors and PCa as the outcome for multivariate MR analysis. According to the statistical results of IVW, after adjusting for direct and indirect bilirubin, total bilirubin (OR:0.74, 95% CI(0.55,0.99), P:0.044) was still significantly protective against PCa, suggesting that total bilirubin is an independent protective factor for PCa. However, after considering both total and indirect bilirubin, the causal relationship between direct bilirubin (OR:0.68, 95% CI(0.43,1.06), P:0.087) and PCa was no longer significant, suggesting that it was not an independent protective factor for PCa. The heterogeneity test suggested that there was no heterogeneity in the instrumental variables. The statistical results of the multivariate MR analysis and heterogeneity test are shown in Fig. 5 . Results of reverse MR analysis In the reverse MR analysis, we screened 71 SNPs with strong correlation with PCa in the European population, and the range of F value was 69.75 to 274.83, indicating that there was no bias of weak instrumental variables, and the detailed information of SNPs can be obtained from Supplementary Table 3. According to the results of IVW, no causal effect of PCa on the six liver function biomarkers was found (P>0.05), indicating a unidirectional causal relationship between ALT, AST and PCa. The statistical results of the reverse MR analysis are displayed in Fig. 6 . Sensitivity analysis suggesting heterogeneity of the instrumental variables was analyzed using the random effects model of IVW. We did not find horizontal pleiotropy in the instrumental variables. The results of the sensitivity analysis are presented in Table 3 . We screened 46 SNPs with strong correlation with PCa in the East Asian population, and the range of F value was 20.92 to 484.07, indicating no bias of weak instrumental variables, and the SNP details can be obtained from Supplementary Table 4. According to the statistical results of IVW, there was a causal relationship between PCa and reduced ALT levels (OR:0.93, 95% CI (0.88,0.98), P:0.007). However, PCa did not affect the levels of other liver function biomarkerss. Sensitivity analyses did not reveal heterogeneity or horizontal pleiotropy (Table 3 ). The statistical results of reverse MR analysis in the East Asian population are displayed in Fig. 6 . Figure 7 shows the scatter plot, funnel plot, and leave-one-out sensitivity analysis plot of PCa to ALT, and the leave-one-out sensitivity analysis did not reveal any abnormal SNPs. (A: scatter plot of PCa to ALT; B: funnel plot of PCa to ALT; C: leave-one-out sensitivity analysis plot of PCa to ALT) Discussion In the prevention and treatment of PCa, liver function biomarkers are not only used as an important evaluation index for the safety of chemotherapeutic agents and endocrine hormone drug therapy [ 26 ], but also have great significance in the monitoring of the process of PCa occurrence and progression. It has been pointed out that the simultaneous observation of PSA and ALP within 1 month after PCa treatment is more able to reflect the effect of tumor shrinkage after treatment than PSA alone [ 27 ]. AST can be used as an important biomarkers of PCa liver metastasis as well as reflecting changes in the volume of hepatic lesions, which can help to alert clinicians to the high-risk group of PCa who have spread to the liver [ 28 , 29 ]. Another study noted that serum ALT showed a negative correlation with PSA levels in healthy men [ 30 ]. Bilirubin is likewise an important biomarkers of liver function. Previous observational studies have described its relationship with PCa. For example, a retrospective analysis of 29,080 Japanese men noted that serum total bilirubin levels showed a U-shaped relationship with the risk of PCa, meaning that either high or low serum total bilirubin increased the risk of PCa [ 31 ]. However, another prospective study based on 15,882 Korean men showed that uric acid was able to increase the risk of PCa and serum total bilirubin was able to decrease its risk [ 32 ], suggesting that serum total bilirubin has a protective effect against PCa. Taken together, whether liver enzymes and bilirubin are associated with the risk of PCa has been the subject of some controversy in past studies. To further elucidate the relationship between liver function biomarkers and PCa, we comprehensively assessed the causal relationship between liver function biomarkers and PCa in six European populations and seven East Asian populations using univariate, multivariate, and bivariate MR analyses and five statistical methods by selecting relevant instrumental variables from a large GWAS database. Our findings form a support to some previous observational studies. Significant differences were found in the causal relationships between liver function biomarkers and PCa in European and East Asian populations. In the European population, only ALT and AST were causally associated with a reduced risk of developing PCa, and the other biomarkers were not. However, in the East Asian population, only total bilirubin and direct bilirubin were causally associated with a reduced risk of PCa, and total bilirubin remained significantly causally associated with PCa after adjusting for direct bilirubin and indirect bilirubin in the multivariate MR analysis. Currently, the mechanisms by which liver enzymes and bilirubin reduce the risk of developing PCa are unclear and fewer studies have been conducted. However, we found that liver lesions, including chronic liver disease and alcohol-related cirrhosis, were shown to reduce serum testosterone levels [ 33 , 34 ]. Therefore, we speculate that liver enzymes and bilirubin may reduce the risk of PCa by lowering testosterone levels. In addition, the accumulation of metabolites such as liver enzymes and bilirubin may also disrupt cancer-related immune and inflammatory signaling pathways and promote changes in the tumor microenvironment [ 35 , 36 ]. Due to the lack of relevant studies, the specific mechanisms by which liver function biomarkers affect PCa need to be further explored. In the reverse MR analysis, we found a causal relationship between PCa and reduced ALT levels in the East Asian population, a phenomenon that does not exist in the European population. Paraneoplastic Syndromes (PNS) are a group of nonspecific symptoms triggered by tumors, which can be manifested as abnormal liver function biomarkers in PCa, and the mechanism may lie in the hormones, cytokines secreted by PCa tumor cells or abnormal immune responses induced by tumors, or damage to hepatocytes in the process of tumor cell metastasis to the liver [ 37 , 38 ]. Several studies have reported cases of PCa with elevated total bilirubin and liver enzymes as the first symptom and advocated PNS as the initial manifestation of metastatic PCa [ 39 , 40 ]. Our study has four strengths. First, univariate, multivariate, and reverse MR analyses were performed using publicly available large-sample GWAS data to comprehensively assess the causal relationship between liver function biomarkers and PCa, and the study was free from confounders and reverse causation, and was effective in saving research costs. Second, we set a series of stringent conditions for instrumental variable screening, used five complementary MR analysis methods to count the causal relationships, and performed sensitivity analysis to ensure the robustness of the results. Third, the study differentiated between European and East Asian ancestry, and compared liver function biomarkers with PCa based on exploring their causal relationship across ancestry. Fourth, the study confirmed the protective effect of liver function biomarkers against PCa, which contributes to disease prevention, monitoring and treatment. There are some limitations of our study. First, PCa is a male-only disease, and the GWAS data on liver function biomarkers in this study were not stratified by sex, which may have led to biased findings. Second, the GWAS data for PCa were not stratified by risk or disease category, including low-risk, intermediate-risk, and high-risk, or hormone-sensitive PCa (HSPC) and depot-resistant PCa (CRPC), resulting in poorly targeted findings. Third, due to fewer studies on the correlation of liver enzymes and bilirubin with PCa, it is difficult to explain the specific mechanism behind the causal relationship, and further cohort studies are needed to determine this. Conclusion We comprehensively assessed the causal relationship between liver function biomarkers and PCa in European and East Asian populations using various forms of MR analysis and statistical methods. The study confirmed that ALT and AST levels were protective factors for PCa in the European population and that PCa did not cause abnormalities in liver function biomarkers. Total bilirubin and direct bilirubin were protective factors for PCa in the East Asian population, in which total bilirubin was an independent influence on the disease, and PCa reduced serum ALT levels. The study not only clarifies the causal relationship between liver function biomarkers and PCa among different pedigrees, but also some liver enzymes and bilirubin, as protective factors of PCa, contribute to early prevention, monitoring and treatment of the disease, providing a new direction for the clinical treatment of PCa. However, the specific mechanisms by which liver function biomarkers affect PCa still need to be further explored. Declarations Ethics approval and consent to participate Not applicable. All data were downloaded from the internet. Consent for publication Not applicable. Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Funding This work was supported by the National Natural Science Foundation of China (82174451) and Specialty Construction Project for the Advantages of Traditional Chinese Medicine/Integrated Chinese and Western Medicine (YW(2023–2024)-01-04). Author Contribution XX conceived and designed the study. XX contributed to data curation. XX and BL contributed to methodology and visualization. XX wrote the original manuscript. WZ and YP revised the article and contributed to the final version of the manuscript. All authors have reviewed and approved the final manuscript. Acknowledgement We thank all of the patients and the investigators who participated in this study. 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Choi WM, Lee JH, Yoon JH, Kwak C, Lee YJ, Cho YY, Lee YB, Yu SJ, Kim YJ, Kim HH, et al. Nonalcoholic fatty liver disease is a negative risk factor for prostate cancer recurrence. Endocr Relat Cancer. 2014;21:343–53. Wang A, Lazo M, Lu J, Couper DJ, Prizment AE, Vitolins MZ, Denmeade SR, Joshu CE, Platz EA. Liver Fibrosis Scores and Prostate Cancer Risk and Mortality in the Atherosclerosis Risk in Communities Study. Cancer Prev Res (Phila). 2023;16:523–30. Wang A, Lazo M, Carter HB, Groopman JD, Nelson WG, Platz EA. Association between Liver Fibrosis and Serum PSA among U.S. Men: National Health and Nutrition Examination Survey (NHANES), 2001–2010. Cancer Epidemiol Biomarkers Prev. 2019;28:1331–8. Qader G, Aali M, Smail SW, Mahmood K, Hasan B, K MA, Rahman DB, Qadir FA, Mohammad DK, Najmuldeen HH, et al. Cardiac, Hepatic and Renal Dysfunction and IL-18 Polymorphism in Breast, Colorectal, and Prostate Cancer Patients. Asian Pac J Cancer Prev. 2021;22:131–7. Katzke V, Johnson T, Sookthai D, Hüsing A, Kühn T, Kaaks R. Circulating liver enzymes and risks of chronic diseases and mortality in the prospective EPIC-Heidelberg case-cohort study. BMJ Open. 2020;10:e033532. Birney E. Mendelian Randomization. Cold Spring Harb Perspect Med 2022, 12. Davies NM, Holmes MV, Davey Smith G. Reading Mendelian randomisation studies: a guide, glossary, and checklist for clinicians. BMJ. 2018;362:k601. Lee CH, Cook S, Lee JS, Han B. Comparison of Two Meta-Analysis Methods: Inverse-Variance-Weighted Average and Weighted Sum of Z-Scores. Genomics Inf. 2016;14:173–80. Burgess S, Thompson SG. Interpreting findings from Mendelian randomization using the MR-Egger method. Eur J Epidemiol. 2017;32:377–89. Su D, Ai Y, Zhu G, Yang Y, Ma P. Genetically predicted circulating levels of cytokines and the risk of osteoarthritis: A mendelian randomization study. Front Genet. 2023;14:1131198. Bowden J, Davey Smith G, Haycock PC, Burgess S. Consistent Estimation in Mendelian Randomization with Some Invalid Instruments Using a Weighted Median Estimator. Genet Epidemiol. 2016;40:304–14. Hartwig FP, Davey Smith G, Bowden J. Robust inference in summary data Mendelian randomization via the zero modal pleiotropy assumption. Int J Epidemiol. 2017;46:1985–98. Windmeijer F, Farbmacher H, Davies N, Davey Smith G. On the Use of the Lasso for Instrumental Variables Estimation with Some Invalid Instruments. J Am Stat Assoc. 2019;114:1339–50. Slob EAW, Groenen PJF, Thurik AR, Rietveld CA. A note on the use of Egger regression in Mendelian randomization studies. Int J Epidemiol. 2017;46:2094–7. Bowden J, Hemani G, Davey Smith G. Invited Commentary: Detecting Individual and Global Horizontal Pleiotropy in Mendelian Randomization-A Job for the Humble Heterogeneity Statistic? Am J Epidemiol. 2018;187:2681–5. Liu M, Yan J, Le K, Li Y, Xing N, Li G. Abiraterone acetate and prednisone in metastatic castration-resistant prostate cancer: a real-world retrospective study in China. Front Endocrinol (Lausanne). 2023;14:1158949. Liaqat M, Khan RA, Fischer F, Kamal S. Relationship between prostate-specific antigen, alkaline phosphatase levels, and time-to-tumor shrinkage: understanding the progression of prostate cancer in a longitudinal study. BMC Urol. 2024;24:137. Cotogno PM, Ranasinghe LK, Ledet EM, Lewis BE, Sartor O. Laboratory-Based Biomarkers and Liver Metastases in Metastatic Castration-Resistant Prostate Cancer. Oncologist. 2018;23:791–7. Ranasinghe L, Cotogno P, Ledet E, Bordlee B, Degeyter K, Nguyen N, Steinberger A, Manogue C, Barata P, Lewis BE, Sartor AO. Relationship between serum markers and volume of liver metastases in castration-resistant prostate cancer. Cancer Treat Res Commun. 2019;20:100151. Han JH, Chang IH, Ahn SH, Kwon OJ, Bang SH, Choi NY, Park SW, Myung SC, Kim HW. Association between serum prostate-specific antigen level, liver function tests and lipid profile in healthy men. BJU Int. 2008;102:1097–101. Inoguchi T, Nohara Y, Nojiri C, Nakashima N. Association of serum bilirubin levels with risk of cancer development and total death. Sci Rep. 2021;11:13224. Kim YR, Choi CK, Lee YH, Choi SW, Kim HY, Shin MH, Kweon SS. Association between Albumin, Total Bilirubin, and Uric Acid Serum Levels and the Risk of Cancer: A Prospective Study in a Korean Population. Yonsei Med J. 2021;62:792–8. Chrysavgis L, Adamantou M, Angelousi A, Cholongitas E. The association of testosterone with sarcopenia and frailty in chronic liver disease. Eur J Clin Invest. 2024;54:e14108. Apostolov R, Wong D, Low E, Vaz K, Spurio J, Worland T, Liu D, Chan RK, Gow P, Grossmann M, Sinclair M. Testosterone is lower in men with non-alcoholic fatty liver disease and alcohol-related cirrhosis and is associated with adverse clinical outcomes. Scand J Gastroenterol. 2023;58:1328–34. Buxton AK, Abbasova S, Bevan CL, Leach DA. Liver Microenvironment Response to Prostate Cancer Metastasis and Hormonal Therapy. Cancers (Basel) 2022, 14. Novysedlak R, Guney M, Al Khouri M, Bartolini R, Koumbas Foley L, Benesova I, Ozaniak A, Novak V, Vesely S, Pacas P et al. The Immune Microenvironment in Prostate Cancer: A Comprehensive Review. Oncology 2024:1–37. Abufaraj M, Ramadan R, Alkhatib A. Paraneoplastic Syndromes in Neuroendocrine Prostate Cancer: A Systematic Review. Curr Oncol. 2024;31:1618–32. Hong MK, Kong J, Namdarian B, Longano A, Grummet J, Hovens CM, Costello AJ, Corcoran NM. Paraneoplastic syndromes in prostate cancer. Nat Rev Urol. 2010;7:681–92. Khanal S, Bhatt T, Atogwe ID, Itare V, Shrestha E, Sulh M. Stauffer Syndrome as the Initial Presentation of Advanced Metastatic Prostate Cancer. Cureus. 2023;15:e37663. Okano A, Ohana M, Kusumi F. Idiopathic cholestatic jaundice may be a paraneoplastic manifestation of underlying malignancy: a case of prostate cancer. Clin J Gastroenterol. 2014;7:278–82. Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterial1.xlsx SupplementaryMaterial2.xlsx SupplementaryMaterial3.xlsx SupplementaryMaterial4.xlsx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 02 Jan, 2025 Reviews received at journal 02 Jan, 2025 Reviewers agreed at journal 23 Dec, 2024 Reviews received at journal 12 Dec, 2024 Reviewers agreed at journal 05 Dec, 2024 Reviewers invited by journal 22 Nov, 2024 Editor assigned by journal 18 Nov, 2024 Submission checks completed at journal 15 Nov, 2024 First submitted to journal 05 Nov, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5396719","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":389529706,"identity":"6d8fe887-f926-4d3d-beee-8fb8ca64eb80","order_by":0,"name":"Xinyu Xu","email":"","orcid":"","institution":"Yueyang Integrated Traditional Chinese and Western Medicine Hospital, Shanghai University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Xinyu","middleName":"","lastName":"Xu","suffix":""},{"id":389529708,"identity":"01b1debc-f42e-4f86-a1c4-0ba84ad98ec9","order_by":1,"name":"Wenjing Zhu","email":"","orcid":"","institution":"Yueyang Integrated Traditional Chinese and Western Medicine Hospital, Shanghai University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Wenjing","middleName":"","lastName":"Zhu","suffix":""},{"id":389529711,"identity":"380ec3ca-1b96-48e3-b9aa-bd7f3100a9b6","order_by":2,"name":"Yu Peng","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0UlEQVRIiWNgGAWjYBACfoYDCQaJ/2x42NibDxCnRbLxwIOCB2xpcvw8xxKI02Jw+OCDjw/YDhtLzvAxINJlxw4nbkjgARI3eD7eeMNgJ6fbQEAHY8+xZIMEifTEDbd7N1vOYUg2NjtAQAuzxJk0gwQD68QNd85uk+ZhOJC4jZAWNvn3338kJDADHZbzjDgtPKBATjjgDPR+DhtxWiTA8dIADmRjyzkGRPjF/sCBBMOfDeCofHjjTYWdHEEtqFbyEBs1SFpI1TEKRsEoGAUjAgAA/EJLCle4Q14AAAAASUVORK5CYII=","orcid":"","institution":"Yueyang Integrated Traditional Chinese and Western Medicine Hospital, Shanghai University of Traditional Chinese Medicine","correspondingAuthor":true,"prefix":"","firstName":"Yu","middleName":"","lastName":"Peng","suffix":""}],"badges":[],"createdAt":"2024-11-05 15:38:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5396719/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5396719/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":71368327,"identity":"0a910f6f-9e25-49f6-bc7c-420d39357fb3","added_by":"auto","created_at":"2024-12-13 18:20:33","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":148320,"visible":true,"origin":"","legend":"\u003cp\u003eDesign idea for MR analysis of two samples\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-5396719/v1/d117676f9eb7fa3e1d76b95c.png"},{"id":71368169,"identity":"105d45e7-6eba-4da3-b77b-725a2b06c196","added_by":"auto","created_at":"2024-12-13 18:12:33","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":149001,"visible":true,"origin":"","legend":"\u003cp\u003eStatistical results of univariate MR analysis\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-5396719/v1/d9ee71bcbd435aa10ed3b634.png"},{"id":71368328,"identity":"9a2c5679-065e-45d1-90c9-e5be0872767e","added_by":"auto","created_at":"2024-12-13 18:20:33","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1816590,"visible":true,"origin":"","legend":"\u003cp\u003eScatter plot, funnel plot and leave-one-out sensitivity analysis of liver function biomarkers to PCa in European population\u003c/p\u003e\n\u003cp\u003e(A: scatter plot of ALT to PCa; B: funnel plot of ALT to PCa; C: leave-one-out sensitivity analysis plot of ALT to PCa; D: scatter plot of AST to PCa; E: funnel plot of AST to PCa; F: leave-one-out sensitivity analysis plot of AST to PCa)\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-5396719/v1/e660b31ff03d0b0b89a0341f.png"},{"id":71368176,"identity":"8c0cd742-75b2-43b9-843a-773b41fd2135","added_by":"auto","created_at":"2024-12-13 18:12:33","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1007385,"visible":true,"origin":"","legend":"\u003cp\u003eScatterplot, funnel plot and leave-one-out sensitivity analysis of liver function biomarkers to PCa in East Asian populations\u003c/p\u003e\n\u003cp\u003e(A: scatter plot of total bilirubin to PCa; B: funnel plot of total bilirubin to PCa; C: leave-one-out sensitivity analysis plot of total bilirubin to PCa; D: scatter plot of direct bilirubin to PCa; E: funnel plot of direct bilirubin to PCa; F: leave-one-out sensitivity analysis plot of direct bilirubin to PCa)\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-5396719/v1/8a3237dd3a18257576fa8c2e.png"},{"id":71368175,"identity":"7896cbae-f128-4f14-a31b-36fa42db4bc0","added_by":"auto","created_at":"2024-12-13 18:12:33","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":37021,"visible":true,"origin":"","legend":"\u003cp\u003eStatistical results of multivariate MR analysis and heterogeneity test\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-5396719/v1/3848d69754c1b21d390cc4d7.png"},{"id":71368173,"identity":"2d54722c-f607-465c-978e-2c04b586229c","added_by":"auto","created_at":"2024-12-13 18:12:33","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":148205,"visible":true,"origin":"","legend":"\u003cp\u003eStatistical results of reverse MR analysis\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-5396719/v1/7b52cba463e0928df2249933.png"},{"id":71368177,"identity":"8ca12b23-49dc-4fe9-88b2-da6339b2f075","added_by":"auto","created_at":"2024-12-13 18:12:33","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":868394,"visible":true,"origin":"","legend":"\u003cp\u003eScatter plot, funnel plot and leave-one-out sensitivity analysis of PCa to ALT in East Asian population\u003c/p\u003e\n\u003cp\u003e(A: scatter plot of PCa to ALT; B: funnel plot of PCa to ALT; C: leave-one-out sensitivity analysis plot of PCa to ALT)\u003c/p\u003e","description":"","filename":"Figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-5396719/v1/39dd1b800176cf3055a30f02.png"},{"id":71368984,"identity":"fa53d19b-26c4-408a-b8c4-4d4c73aa3ec5","added_by":"auto","created_at":"2024-12-13 18:28:37","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5152415,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5396719/v1/f299f446-097d-4e33-8a8e-a5e9fe7d6683.pdf"},{"id":71368174,"identity":"5a396e22-bdab-4ffd-b4d4-0377524ca6a3","added_by":"auto","created_at":"2024-12-13 18:12:33","extension":"xlsx","order_by":10,"title":"","display":"","copyAsset":false,"role":"supplement","size":281076,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5396719/v1/38dd7d23710f05107a20f5bf.xlsx"},{"id":71368171,"identity":"1be298d1-4442-4ac6-a4ee-8586e02296ac","added_by":"auto","created_at":"2024-12-13 18:12:33","extension":"xlsx","order_by":11,"title":"","display":"","copyAsset":false,"role":"supplement","size":25443,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5396719/v1/c6681a961492be23875d2451.xlsx"},{"id":71368178,"identity":"cd93db10-fd92-4de3-abac-ef894493d3c4","added_by":"auto","created_at":"2024-12-13 18:12:33","extension":"xlsx","order_by":12,"title":"","display":"","copyAsset":false,"role":"supplement","size":15972,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial3.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5396719/v1/158e7881284495c35c225d0b.xlsx"},{"id":71368180,"identity":"34513bdf-0cc0-4e57-90de-5cc0b1afe7ac","added_by":"auto","created_at":"2024-12-13 18:12:34","extension":"xlsx","order_by":13,"title":"","display":"","copyAsset":false,"role":"supplement","size":14596,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial4.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5396719/v1/e39538037725c1cad145e2cd.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Causal Associations Between Liver Function Biomarkers and Prostate Cancer Risk in European and East Asian Populations: A Univariate, Multivariate, and Bidirectional Mendelian Randomization Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eProstate cancer (PCa) is a common cancer worldwide. 1.41\u0026nbsp;million new cases of PCa and 380,000 deaths occurred worldwide in 2020, ranking third and eighth in terms of incidence and mortality of cancers, seriously jeopardizing human health and safety worldwide [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. PCa incidence is anomalous between ancestries due to differences in genetic ancestry. The higher risk of PCa in men of African ancestry compared to men of European and Asian ancestry [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], and the higher incidence of PCa in African Americans (AAs) compared to European Americans (EAs) suggests that genetic factors are involved in the development of PCa [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. In Europe and the United States, PCa is the most common malignant tumor in men.2021 American Cancer Society statistics show that PCa accounts for about 27% of the number of men with tumor prevalence [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Although the incidence of PCa in Asian countries is lower than that in Europe and the United States, it has also shown a rapid rising trend in recent years [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. As PCa has no obvious symptoms in the early stage, most patients are in the middle or late stage when they are diagnosed. The 5-year survival rate of Chinese PCa patients is only 66.4% [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], and the 5-year survival rate of PCa patients with distant metastases is only 31% [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Currently, the most common treatments for PCa include active monitoring of prostate-specific antigen (PSA) as well as chemotherapy, radiotherapy, hormonal and surgical treatments. However, PSA has a high rate of false positives and unsupported results, while these tools have limited efficacy in recurrent, drug-resistant and metastatic PCa [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAbnormal liver function may affect the pathophysiology of PCa, but there is some controversy. It was noted that non-alcoholic fatty liver disease (NAFLD) was an independent risk factor for biochemical recurrence in patients with high-grade metastatic PCa, with a 2-year biochemical recurrence rate of 84.0% and 72.2% in patients with and without NAFLD with a Gleason score of \u0026ge;\u0026thinsp;4\u0026thinsp;+\u0026thinsp;3, and a median biochemical recurrence-free survival of 17 months and 21 months, respectively [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. In contrast, another study concluded that NAFLD is protective against biochemical recurrence after radical PCa surgery [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. In addition, higher liver fibrosis scores were associated with lower PCa incidence in black men and not in white men [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Men with higher liver fibrosis scores typically have lower PSA levels, and thus men with abnormal liver function have the potential for delayed PCa surveillance [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Liver function markers reflect hepatocyte integrity, cholestasis, and hepatic synthetic function, and include alanine aminotransferase (ALT), aspartate aminotransferase (AST), alkaline phosphatase (ALP), gamma-glutamyl transpeptidase (GGT), and bilirubin. Abnormal liver function is usually characterized by elevated or decreased levels of these markers. Studies have shown that ALT, AST, and ALP levels are significantly elevated in patients with PCa compared to healthy controls, and that liver enzyme concentrations have the potential to be used as a surrogate biomarkers for PCa diagnosis [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. A different view was presented by another study, who suggested that the De Ritis ratio (AST/ALT) increased the incidence of PCa, but there was no correlation between AST and ALT and the risk of developing PCa [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. We believe that observational studies may be confounded by confounding factors and reverse causality, resulting in heterogeneity of findings. Therefore, more advanced research methods are needed to confirm the relationship between liver function biomarkers and PCa.\u003c/p\u003e \u003cp\u003eMendelian randomization (MR) is an analytical method for assessing the causal relationship between exposure factors and outcomes. The core principle is based on Mendel's second law of inheritance, which ensures that gene-disease associations are not interfered with by confounding factors such as postnatal environment and behaviors, and that the order of causality is reasonable, which can make up for the shortcomings of observational studies [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], and provides a new method for assessing the relationship between liver function biomarkers and PCa. In this study, we used multiple MR analyses to assess the causal relationship between liver function biomarkers and PCa in a more comprehensive manner and compared the differences between European and East Asian populations, thus providing some evidence for the prevention and monitoring of the course of PCa.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData sources\u003c/h2\u003e \u003cp\u003eIn this study, several liver function biomarkers were selected as exposure and PCa as outcome, and European and Asian populations were differentiated. Six liver function biomarkers were selected as exposures in the European population, including alanine aminotransferase (ALT), alanine oxaloacetate aminotransferase (AST), glutamylglutamyltransferase (GGT), alkaline phosphatase (ALP), total bilirubin, and direct bilirubin, all of which were derived from the data of a genome-wide association study published by Neale lab. Pooled data on PCa in the European population were published by the Finnish database R11 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.finngen.fi/\u003c/span\u003e\u003cspan address=\"https://www.finngen.fi/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and included 17,258 cases and 143,624 controls. Detailed data on exposure and outcome for the European population can be obtained from Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Seven liver function biomarkers were selected as exposures for the East Asian population, including ALT, AST, GGT, ALP, total bilirubin, direct bilirubin, and indirect bilirubin, which were all published by the Pan-UKB team. GWAS data for PCa in the East Asian population were published by Ishigaki K et al. and included 5,408 cases and 103,939 controls. Detailed data on exposure and outcomes in the East Asian population can be obtained from Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The data for this study were obtained from publicly available databases and therefore did not require ethical approval or informed consent.\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\u003eInformation on Population Exposure and Outcome Data in Europe\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTraits\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNumber of SNPs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYear\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePopulation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eGWASID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSource\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003eExposure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAlanine aminotransferase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13,586,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eukb-d-30620_irnt\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://gwas.mrcieu.ac.uk/\u003c/span\u003e\u003cspan address=\"https://gwas.mrcieu.ac.uk/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAspartate aminotransferase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13,586,009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eukb-d-30650_irnt\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGamma glutamyltransferase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13,586,026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eukb-d-30730_irnt\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAlkaline phosphatase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13,586,006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eukb-d-30610_irnt\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal bilirubin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13,585,986\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eukb-d-30840_irnt\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDirect bilirubin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13,584,679\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eukb-d-30660_irnt\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOutcome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProstate cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20,429,334\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eFinnGen R11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.finngen.fi/\u003c/span\u003e\u003cspan address=\"https://www.finngen.fi/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\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\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\u003eInformation on exposure and outcome data for East Asian populations\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\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=\"char\" char=\".\" 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=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTraits\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003encase\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003encontrol\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYear\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePopulation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eGWASID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSource\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003eExposure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAlanine aminotransferase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,570\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEast Asian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eukb-e-30620_EAS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"7\" rowspan=\"8\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://gwas.mrcieu.ac.uk/\u003c/span\u003e\u003cspan address=\"https://gwas.mrcieu.ac.uk/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAspartate aminotransferase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,551\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEast Asian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eukb-e-30650_EAS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGamma glutamyltransferase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,569\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEast Asian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eukb-e-30730_EAS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAlkaline phosphatase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,572\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEast Asian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eukb-e-30610_EAS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal bilirubin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,549\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEast Asian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eukb-e-30840_EAS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDirect bilirubin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,159\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEast Asian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eukb-e-30660_EAS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIndirect bilirubin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,165\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEast Asian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eukb-e-recode1_EAS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOutcome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProstate cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5,408\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e103,939\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEast Asian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ebbj-a-148\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\n\u003ch3\u003eStudy design\u003c/h3\u003e\n\u003cp\u003eIn this study, we used univariate, multivariate and bidirectional MR analyses to fully assess the causal relationship between liver function biomarkers and PCa. In the first step, we used univariate MR to analyze the causal relationship between liver function biomarkers and PCa using liver function biomarkers as exposures and PCa as outcomes, and labeled the liver function biomarkers that were causally related to PCa; in the second step, based on the labeled liver function biomarkers, we selected appropriate multiple exposures for multivariate MR analysis to determine whether these liver function biomarkers could serve as an independent influencing factor for PCa; In the third step, using PCa as the exposure factor and all liver function biomarkers as the outcome, reverse MR was used to analyze whether PCa leads to changes in liver function biomarkers, thus determining whether there is a reverse causal association. This study followed three main hypotheses: 1) Association hypothesis. SNP as an instrumental variable must be strongly correlated with exposure factors; 2) Independence assumption. SNP cannot be interfered with by other confounding factors that can affect exposure and outcome; 3) Exclusivity assumption. SNP affects outcome only through exposure factors and not through other pathways. The idea of this study is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eSelection of instrumental variables\u003c/h3\u003e\n\u003cp\u003eSNPs obtained from GWAS data were used as instrumental variables in this study. First, to ensure that the included SNPs had strong correlation with liver function biomarkers (exposure factors), we set a series of screening criteria. We set P\u0026thinsp;\u0026le;\u0026thinsp;5\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e in GWAS data for liver function biomarkers in European populations for SNP screening. Because only 1 SNP was obtained for some exposure factors, such as AST, when P\u0026thinsp;\u0026le;\u0026thinsp;5\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e was used in the East Asian population, we set P\u0026thinsp;\u0026le;\u0026thinsp;5\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e in the GWAS data for liver function biomarkers in the East Asian population for SNP screening. Second, in order to ensure that SNPs are not interfered by confounding factors, we performed the process of removing linkage disequilibrium by setting the linkage disequilibrium coefficient r2\u0026thinsp;=\u0026thinsp;0.001, and the width of linkage disequilibrium region is 10,000 kb.Third, the echo SNPs with unclear identity of the effector allele were removed.Lastly, in order to avoid the bias caused by weak instrumental variables, we used the F statistic to measure instrumental variables' strength, and F\u0026gt;10 was considered to be free of bias from weak instrumental variables [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].The formula for F is shown below with the relevant variables labeled:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:\\text{F}＝{{\\beta\\:}}^{2}/{SE}^{2}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eβ is the effect value of the allele and SE is the standard error.\u003c/p\u003e \u003cp\u003eIn the reverse MR analysis, we used PCa as an exposure factor and liver function index as an outcome to explore the reverse causality between them. The screening conditions were consistent with the forward MR analysis, i.e., P\u0026thinsp;\u0026le;\u0026thinsp;5\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e, r2\u0026thinsp;=\u0026thinsp;0.001, and kb\u0026thinsp;=\u0026thinsp;10,000 for the European population, and P\u0026thinsp;\u0026le;\u0026thinsp;5\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e, r2\u0026thinsp;=\u0026thinsp;0.001, and kb\u0026thinsp;=\u0026thinsp;10,000 for the East Asian population.In addition, the screening conditions for the multivariate MR analysis were also consistent with the forward MR analysis.\u003c/p\u003e\n\u003ch3\u003eMR analysis\u003c/h3\u003e\n\u003cp\u003eUnivariate, multivariate, and reverse MR were all analyzed using the inverse variance weighted (IVW) method as the main analytical method, and the IVW method is the most widely used in MR analyses, where the Wald values for each SNP are combined to produce causal estimates for each risk when all instrumental variables are valid [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. When there is no heterogeneity in the results, a fixed-effects model is used. Conversely a random effects model was used. However, IVW requires that all instrumental variables must be valid, and IVW estimates may be biased when the included SNPs may be invalid instrumental variables. Therefore, in univariate and reverse MR, we added the weighted median (WM) method as well as MR-Egger regression, weighted mode, and simple mode to increase the robustness of the results. WM is robust to anomalous SNPs because it uses the weighted median of the effect sizes. When at least 50% of the instrumental variables are valid, WM provides robust estimates, reducing potential bias due to a single weak or invalid instrumental variable [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. MR-Egger regression detects and accommodates multinomiality of instrumental variables and presents the results through a p-value, with P\u0026lt;0.05 indicating the presence of multinomiality [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], which at this point provides a less biased effect than IVW estimation [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Weighted models are used to assess the overall causal effect of a large number of genetic instruments, producing lower Type I error rates, less bias, and less statistical power than IVW methods. The simple model calculates the relationship between individual IVs and outcomes, providing intuitive and easy causal inference [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. In multivariate MR analyses, the Least Absolute Shrinkage and Selection Operator (LASSO) is used in addition to MR-Egger, which is able to perform variable selection and regularization, which helps to identify the variables that have a significant effect on the outcome variable when there is a large number of potential predictor variables, and improves the accuracy and interpretability of model predictions [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eSensitivity analysis\u003c/h3\u003e\n\u003cp\u003eMR-Egger and MR pleiotropy residual sum and outlier (MR pleiotropy residual sum and outlier, MR-PRESSO) were used together to test the multivariate validity of the instrumental variables, and MR-PRESSO's global test was used to detect the horizontal multivariate validity of instrumental variables, with P\u0026gt;0.05 representing there is no polyvalence. When the global test suggests polytropy, the distortion test in MR-PRESSO detects outliers and calculates whether there is a difference in the results before and after removing the outliers, thus reducing the effect of outliers on the statistical results [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Heterogeneity of instrumental variables was assessed by Cochran's Q statistic, with P\u0026lt;0.05 indicating the presence of heterogeneity [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Leave-one-out sensitivity analyses were used to assess whether the presence of bias due to a single SNP affected the overall causal effect.\u003c/p\u003e \u003cp\u003eOdds ratios (OR) and 95% confidence intervals (CI) were used to estimate the relative risk triggered by the presence of exposure, and differences were considered statistically significant at P\u0026lt;0.05. All statistics were processed using R4.3.2 TwoSampleMR calculations with a test level of α\u0026thinsp;=\u0026thinsp;0.05.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eResults of univariate MR analysis\u003c/h2\u003e \u003cp\u003eAfter rigorous screening, we obtained SNPs with strong correlation with liver function biomarkers in the European population and calculated the F value. Among them, 179 SNPs were strongly correlated with ALT, with F values ranging from 29.80 to 1025.34. 212 SNPs were strongly correlated with AST, with F values ranging from 29.77 to 3608.30. 257 SNPs were strongly correlated with GGT, with F values ranging from 29.73 to 2463.02. 275 SNPs were strongly correlated with ALP, with F values ranging from 29.75 to 1362.05. 115 SNPs were strongly correlated with ALP, with F values ranging from 29.75 to 1362.05. 115 SNPs were strongly correlated with total bilirubin, with F values ranging from 29.80 to 15029.21. 62 SNPs were strongly correlated with direct bilirubin, with F values ranging from 29.76 to 31283.91. All SNPs showed sufficient statistical efficacy (F\u0026gt;10) to indicate the absence of bias from a weak instrumental variable. SNP details can be obtained from the Supplementary Table\u0026nbsp;1. In the univariate MR analysis, we used the statistical results of IVW as the main basis. The results showed a causal relationship between ALT (OR:0.85, 95% CI(0.75,0.95), P:0.005) and AST (OR:0.90, 95% CI(0.81,1.00), P:0.045) and a reduced risk of PCa, whereas GGT, ALP, total bilirubin, and direct bilirubin did not have a causal relationship (P\u0026gt;0.05), and the results of other method statistics were not exactly the same as IVW. However, IVW was used as the primary method in our study, so the estimation of the causal effect was reliable. The statistical results of univariate MR analysis are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e demonstrates the scatterplot, funnel plot, and leave-one-out sensitivity analysis plot of liver function biomarkers to PCa. In the sensitivity analysis, heterogeneity of the instrumental variables was calculated by using Cochran's Q. Therefore, the random-effects model of IVW was used for the analysis. MR-Egger and MR-PRESSO both suggested that there was no horizontal multinomiality in the instrumental variables (P\u0026gt;0.05). The results of the sensitivity analyses are presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The leave-one-out sensitivity analysis did not find any SNPs significantly affecting the results (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), indicating robust results.\u003c/p\u003e \u003cp\u003eAmong the liver function biomarkers in East Asian population, 9 SNPs were strongly correlated with ALT after screening, with F-values ranging from 20.97 to 24.90. 2 SNPs were strongly correlated with AST, with F-values ranging from 21.00 to 22.92. 10 SNPs were strongly correlated with GGT, with F-values ranging from 20.86 to 79.79. 4 SNPs were strongly correlated with ALP, with F-values ranging from 21.09 to 80.67. 8 SNPs were strongly correlated with total bilirubin, with F values ranging from 21.35 to 292.08. 7 SNPs were strongly correlated with direct bilirubin, with F values ranging from 22.21 to 183.58. 5 SNPs were strongly correlated with indirect bilirubin, with F values ranging from 20.90 to 229.42. All the SNPs demonstrated sufficient statistical efficacy (F\u0026gt;10), and there were no weak instrumental variables. SNP details are available from Supplementary Table\u0026nbsp;2. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, univariate MR analysis showed that there was a causal relationship between total bilirubin (OR:0.94, 95% CI(0.88,1.00), P:0.049) and direct bilirubin (OR:0.91, 95% CI(0.84,0.99), P:0.022) and the reduced risk of PCa, and that there was no causal relationship between ALT, AST, GGT, ALP and indirect bilirubin were not causally associated with the risk of PCa (P\u0026gt;0.05). Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e demonstrates scatterplots, funnel plots, and leave-one-out sensitivity analysis plots of liver function biomarkers with PCa. The sensitivity analyses did not reveal the presence of heterogeneity or horizontal pleiotropy (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e(A: scatter plot of ALT to PCa; B: funnel plot of ALT to PCa; C: leave-one-out sensitivity analysis plot of ALT to PCa; D: scatter plot of AST to PCa; E: funnel plot of AST to PCa; F: leave-one-out sensitivity analysis plot of AST to PCa)\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e(A: scatter plot of total bilirubin to PCa; B: funnel plot of total bilirubin to PCa; C: leave-one-out sensitivity analysis plot of total bilirubin to PCa; D: scatter plot of direct bilirubin to PCa; E: funnel plot of direct bilirubin to PCa; F: leave-one-out sensitivity analysis plot of direct bilirubin to PCa)\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\u003eResults of sensitivity analysis for univariate and reverse MR\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePopulation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eExposure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eOutcome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eHeterogeneity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003ePleiotropy\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIVW_Q\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eQ_pval\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMR-Egger\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMR-PRESSO\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"11\" rowspan=\"12\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAlanine aminotransferase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003eProstate cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e301.231\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.213E-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.487\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.675\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAspartate aminotransferase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e408.916\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.845E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.712\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.850\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGamma glutamyltransferase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e576.183\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.036E-26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.253\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.877\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAlkaline phosphatase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e527.401\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.129E-18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.910\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.523\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal bilirubin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e249.280\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.253E-12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.323\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.943\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDirect bilirubin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e115.074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.509E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.963\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.883\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003eProstate cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAlanine aminotransferase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e163.288\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.252E-10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.089\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.537\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAspartate aminotransferase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e128.840\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.204E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.629\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.800\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGamma glutamyltransferase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e233.836\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.641E-20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.303\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.400\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAlkaline phosphatase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e258.345\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.723E-24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.606\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.900\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTotal bilirubin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e139.519\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.459E-07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.368\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDirect bilirubin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e130.113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.735E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.902\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.220\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"13\" rowspan=\"14\"\u003e \u003cp\u003eEast Asian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAlanine aminotransferase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003eProstate cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.293\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.806\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.117\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAspartate aminotransferase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.667\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.414\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGamma glutamyltransferase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.874\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.674\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.097\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAlkaline phosphatase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.885\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.487\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.215\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal bilirubin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.687\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.698\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.405\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.767\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDirect bilirubin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.677\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.808\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.770\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIndirect bilirubin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.239\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.264\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.533\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.367\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003eProstate cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAlanine aminotransferase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38.729\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.615\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.281\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.587\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAspartate aminotransferase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27.665\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.957\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.996\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.963\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGamma glutamyltransferase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e46.942\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.277\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.475\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.320\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAlkaline phosphatase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39.039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.602\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.615\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTotal bilirubin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.998\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.818\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDirect bilirubin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23.112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.992\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.723\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIndirect bilirubin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21.211\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.997\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.217\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.997\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\n\u003ch3\u003eResults of multivariate MR analysis\u003c/h3\u003e\n\u003cp\u003eIn multivariate MR analysis, we minimized the use of exposure factors in order to avoid the problem of covariance caused by multiple exposures. In the European population, we chose ALT, AST and GGT as exposure factors and PCa as outcome for multivariate MR analysis. According to the IVW statistics, the protective effect of ALT (OR:0.18, 95% CI(0.71,1.07), P:0.183) and AST (OR:0.83, 95% CI(0.84,1.15), P:0.827) on PCa was no longer significant after the other two biomarkers of liver function were also considered. This finding suggests that the protective effect of ALT and AST on PCa may not be independent of other biomarkers of liver health. That is, they are not independent protective factors for PCa. The heterogeneity test suggested that the instrumental variables were not heterogeneous.\u003c/p\u003e \u003cp\u003eAlso due to the covariance issue, in the East Asian population, we chose total bilirubin, direct bilirubin and indirect bilirubin as the exposure factors and PCa as the outcome for multivariate MR analysis. According to the statistical results of IVW, after adjusting for direct and indirect bilirubin, total bilirubin (OR:0.74, 95% CI(0.55,0.99), P:0.044) was still significantly protective against PCa, suggesting that total bilirubin is an independent protective factor for PCa. However, after considering both total and indirect bilirubin, the causal relationship between direct bilirubin (OR:0.68, 95% CI(0.43,1.06), P:0.087) and PCa was no longer significant, suggesting that it was not an independent protective factor for PCa. The heterogeneity test suggested that there was no heterogeneity in the instrumental variables.\u003c/p\u003e \u003cp\u003eThe statistical results of the multivariate MR analysis and heterogeneity test are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eResults of reverse MR analysis\u003c/h2\u003e \u003cp\u003eIn the reverse MR analysis, we screened 71 SNPs with strong correlation with PCa in the European population, and the range of F value was 69.75 to 274.83, indicating that there was no bias of weak instrumental variables, and the detailed information of SNPs can be obtained from Supplementary Table\u0026nbsp;3. According to the results of IVW, no causal effect of PCa on the six liver function biomarkers was found (P\u0026gt;0.05), indicating a unidirectional causal relationship between ALT, AST and PCa. The statistical results of the reverse MR analysis are displayed in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e. Sensitivity analysis suggesting heterogeneity of the instrumental variables was analyzed using the random effects model of IVW. We did not find horizontal pleiotropy in the instrumental variables. The results of the sensitivity analysis are presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eWe screened 46 SNPs with strong correlation with PCa in the East Asian population, and the range of F value was 20.92 to 484.07, indicating no bias of weak instrumental variables, and the SNP details can be obtained from Supplementary Table\u0026nbsp;4. According to the statistical results of IVW, there was a causal relationship between PCa and reduced ALT levels (OR:0.93, 95% CI (0.88,0.98), P:0.007). However, PCa did not affect the levels of other liver function biomarkerss. Sensitivity analyses did not reveal heterogeneity or horizontal pleiotropy (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The statistical results of reverse MR analysis in the East Asian population are displayed in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e. Figure\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e shows the scatter plot, funnel plot, and leave-one-out sensitivity analysis plot of PCa to ALT, and the leave-one-out sensitivity analysis did not reveal any abnormal SNPs.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e(A: scatter plot of PCa to ALT; B: funnel plot of PCa to ALT; C: leave-one-out sensitivity analysis plot of PCa to ALT)\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn the prevention and treatment of PCa, liver function biomarkers are not only used as an important evaluation index for the safety of chemotherapeutic agents and endocrine hormone drug therapy [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], but also have great significance in the monitoring of the process of PCa occurrence and progression. It has been pointed out that the simultaneous observation of PSA and ALP within 1 month after PCa treatment is more able to reflect the effect of tumor shrinkage after treatment than PSA alone [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. AST can be used as an important biomarkers of PCa liver metastasis as well as reflecting changes in the volume of hepatic lesions, which can help to alert clinicians to the high-risk group of PCa who have spread to the liver [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Another study noted that serum ALT showed a negative correlation with PSA levels in healthy men [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Bilirubin is likewise an important biomarkers of liver function. Previous observational studies have described its relationship with PCa. For example, a retrospective analysis of 29,080 Japanese men noted that serum total bilirubin levels showed a U-shaped relationship with the risk of PCa, meaning that either high or low serum total bilirubin increased the risk of PCa [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. However, another prospective study based on 15,882 Korean men showed that uric acid was able to increase the risk of PCa and serum total bilirubin was able to decrease its risk [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], suggesting that serum total bilirubin has a protective effect against PCa. Taken together, whether liver enzymes and bilirubin are associated with the risk of PCa has been the subject of some controversy in past studies.\u003c/p\u003e \u003cp\u003eTo further elucidate the relationship between liver function biomarkers and PCa, we comprehensively assessed the causal relationship between liver function biomarkers and PCa in six European populations and seven East Asian populations using univariate, multivariate, and bivariate MR analyses and five statistical methods by selecting relevant instrumental variables from a large GWAS database. Our findings form a support to some previous observational studies. Significant differences were found in the causal relationships between liver function biomarkers and PCa in European and East Asian populations. In the European population, only ALT and AST were causally associated with a reduced risk of developing PCa, and the other biomarkers were not. However, in the East Asian population, only total bilirubin and direct bilirubin were causally associated with a reduced risk of PCa, and total bilirubin remained significantly causally associated with PCa after adjusting for direct bilirubin and indirect bilirubin in the multivariate MR analysis. Currently, the mechanisms by which liver enzymes and bilirubin reduce the risk of developing PCa are unclear and fewer studies have been conducted. However, we found that liver lesions, including chronic liver disease and alcohol-related cirrhosis, were shown to reduce serum testosterone levels [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Therefore, we speculate that liver enzymes and bilirubin may reduce the risk of PCa by lowering testosterone levels. In addition, the accumulation of metabolites such as liver enzymes and bilirubin may also disrupt cancer-related immune and inflammatory signaling pathways and promote changes in the tumor microenvironment [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Due to the lack of relevant studies, the specific mechanisms by which liver function biomarkers affect PCa need to be further explored.\u003c/p\u003e \u003cp\u003eIn the reverse MR analysis, we found a causal relationship between PCa and reduced ALT levels in the East Asian population, a phenomenon that does not exist in the European population. Paraneoplastic Syndromes (PNS) are a group of nonspecific symptoms triggered by tumors, which can be manifested as abnormal liver function biomarkers in PCa, and the mechanism may lie in the hormones, cytokines secreted by PCa tumor cells or abnormal immune responses induced by tumors, or damage to hepatocytes in the process of tumor cell metastasis to the liver [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Several studies have reported cases of PCa with elevated total bilirubin and liver enzymes as the first symptom and advocated PNS as the initial manifestation of metastatic PCa [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOur study has four strengths. First, univariate, multivariate, and reverse MR analyses were performed using publicly available large-sample GWAS data to comprehensively assess the causal relationship between liver function biomarkers and PCa, and the study was free from confounders and reverse causation, and was effective in saving research costs. Second, we set a series of stringent conditions for instrumental variable screening, used five complementary MR analysis methods to count the causal relationships, and performed sensitivity analysis to ensure the robustness of the results. Third, the study differentiated between European and East Asian ancestry, and compared liver function biomarkers with PCa based on exploring their causal relationship across ancestry. Fourth, the study confirmed the protective effect of liver function biomarkers against PCa, which contributes to disease prevention, monitoring and treatment.\u003c/p\u003e \u003cp\u003eThere are some limitations of our study. First, PCa is a male-only disease, and the GWAS data on liver function biomarkers in this study were not stratified by sex, which may have led to biased findings. Second, the GWAS data for PCa were not stratified by risk or disease category, including low-risk, intermediate-risk, and high-risk, or hormone-sensitive PCa (HSPC) and depot-resistant PCa (CRPC), resulting in poorly targeted findings. Third, due to fewer studies on the correlation of liver enzymes and bilirubin with PCa, it is difficult to explain the specific mechanism behind the causal relationship, and further cohort studies are needed to determine this.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eWe comprehensively assessed the causal relationship between liver function biomarkers and PCa in European and East Asian populations using various forms of MR analysis and statistical methods. The study confirmed that ALT and AST levels were protective factors for PCa in the European population and that PCa did not cause abnormalities in liver function biomarkers. Total bilirubin and direct bilirubin were protective factors for PCa in the East Asian population, in which total bilirubin was an independent influence on the disease, and PCa reduced serum ALT levels. The study not only clarifies the causal relationship between liver function biomarkers and PCa among different pedigrees, but also some liver enzymes and bilirubin, as protective factors of PCa, contribute to early prevention, monitoring and treatment of the disease, providing a new direction for the clinical treatment of PCa. However, the specific mechanisms by which liver function biomarkers affect PCa still need to be further explored.\u003c/p\u003e"},{"header":"Declarations","content":" \u003cp\u003e \u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e \u003cp\u003eNot applicable. All data were downloaded from the internet.\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 that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis work was supported by the National Natural Science Foundation of China (82174451) and Specialty Construction Project for the Advantages of Traditional Chinese Medicine/Integrated Chinese and Western Medicine (YW(2023\u0026ndash;2024)-01-04).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eXX conceived and designed the study. XX contributed to data curation. XX and BL contributed to methodology and visualization. XX wrote the original manuscript. WZ and YP revised the article and contributed to the final version of the manuscript. All authors have reviewed and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe thank all of the patients and the investigators who participated in this study.\u003c/p\u003e\u003ch2\u003eAvailability of data and materials\u003c/h2\u003e \u003cp\u003eAll data generated or analyzed during this study are included in this published article and its supplementary information files.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, Bray F. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin. 2021;71:209\u0026ndash;49.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTimmins IR, Dudbridge F. 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Clin J Gastroenterol. 2014;7:278\u0026ndash;82.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"dion","sideBox":"Learn more about [Discover Oncology](https://www.springer.com/12672)","snPcode":"","submissionUrl":"","title":"Discover Oncology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"prostate cancer, liver function biomarkers, Mendelian randomization, causality","lastPublishedDoi":"10.21203/rs.3.rs-5396719/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5396719/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eTo investigate the causal relationship between liver function biomarkers and prostate cancer (PCa) in European and East Asian populations using various forms of Mendelian Randomization (MR) and statistical analyses.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eSingle nucleotide polymorphism (SNP), which is strongly associated with exposure factors, was selected as an instrumental variable from genome-wide association studies (GWAS). Inverse variance weighting (IVW), weighted median (WM), MR-Egger, weighted mode, and simple mode were used to investigate the causal relationship between exposure and outcome, and sensitivity analyses were performed to verify the robustness of the results.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eUnivariate MR analysis suggested a causal relationship between ALT (OR:0.85, 95% CI(0.75,0.95), P:0.005) and AST (OR:0.90, 95% CI(0.81,1.00), P:0.045) and a reduced risk of PCa in European populations, and a causal relationship between total bilirubin (OR:0.94, 95% CI(0.88,1.00), P:0.049) and direct bilirubin (OR:0.91, 95% CI(0.84,0.99), P:0.022) were causally associated with reduced PCa risk in the East Asian population. The association between total bilirubin (OR:0.74, 95% CI(0.55,0.99), P:0.044) and PCa remained significant after multivariate MR analysis adjusting for confounders. In the reverse MR analysis, a causal relationship between PCa and reduced ALT (OR:0.93, 95% CI(0.88,0.98), P:0.007) was found only in the East Asian population. Sensitivity analyses did not reveal heterogeneity or horizontal pleiotropy.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThere are differences in the causal relationship between liver function biomarkers and PCa in European and East Asian populations. ALT and AST are protective factors for PCa in European populations, and total bilirubin and direct bilirubin in East Asian populations. PCa decreases ALT levels in East Asian populations, which may be one of the characteristic manifestations of PCa paraneoplastic syndrome. Overall, these findings provide ideas for clinical prevention, monitoring and treatment of PCa.\u003c/p\u003e","manuscriptTitle":"Causal Associations Between Liver Function Biomarkers and Prostate Cancer Risk in European and East Asian Populations: A Univariate, Multivariate, and Bidirectional Mendelian Randomization Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-13 18:12:28","doi":"10.21203/rs.3.rs-5396719/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-01-03T04:46:58+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-01-02T12:32:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"132882845385273790413817336333472634040","date":"2024-12-24T04:13:24+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-12-12T13:02:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"264589503173749592609608994341169181060","date":"2024-12-05T12:26:38+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-11-22T09:26:41+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-11-18T11:02:41+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-11-16T03:39:45+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Oncology","date":"2024-11-05T15:26:09+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"dion","sideBox":"Learn more about [Discover Oncology](https://www.springer.com/12672)","snPcode":"","submissionUrl":"","title":"Discover Oncology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9584b469-53c2-4395-a308-60078317eb4f","owner":[],"postedDate":"December 13th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-03-20T06:08:12+00:00","versionOfRecord":[],"versionCreatedAt":"2024-12-13 18:12:28","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5396719","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5396719","identity":"rs-5396719","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}