Causal Relationship Among Intestinal Microbiota, Lipid Metabolites, and Cholangiocarcinoma: A Mendelian Randomization Study

Causal Relationship Among Intestinal Microbiota, Lipid Metabolites, and Cholangiocarcinoma: A 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 Relationship Among Intestinal Microbiota, Lipid Metabolites, and Cholangiocarcinoma: A Mendelian Randomization Study Sicheng Xu, Xing He, Liqiang Liu, Junkai Ren, Qixian Zhou, Huilin Ye, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4687408/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Cholangiocarcinoma (CCA) is an aggressive tumor with a poor prognosis. Numerous animal experiments and clinical studies have indicated an association between the gut microbiota (GM) and the incidence of CCA. Additionally, patients with CCA often exhibit metabolic disorders, but there is a lack of evidence regarding causality. Therefore, elucidating the causal relationships among GM, plasma metabolites, and CCA is crucial and can provide insights for the prevention and treatment of CCA. Objective: We utilized summary statistics from the largest available genome-wide association studies, including gut microbiota (GM) data from the MiBioGen consortium (n = 18,340), plasma metabolites from four distinct human metabolomics studies, and cholangiocarcinoma (CCA) patient data from the UK Biobank (cases n = 832 and controls n = 475,259). We conducted bidirectional Mendelian randomization analyses to explore the causal relationship between GM and CCA. Additionally, we performed two mediation analyses and a two-step Mendelian randomization (MR) to identify potential mediating metabolites, offering guidance for the clinical early detection and intervention of CCA. Results: In our analysis, we identified that two types of gut microbes (Enterobacteriaceae and Enterobacteriales) increase the risk of cholangiocarcinoma (CCA), while eight types of gut microbes, including Lachnospiraceae and Eggerthella, have protective effects. Additionally, we identified 31 plasma metabolites significantly associated with CCA, with lipid metabolism disorders being a key factor. Notably, four plasma metabolites, such as Intermediate-Density Lipoprotein Triglycerides (IDL_TG), mediate the relationship between gut microbiota and CCA, highlighting the role of plasma metabolites as intermediaries. These findings underscore the potential of targeting gut microbiota and plasma metabolites for the prevention and treatment of CCA. Conclusion: Our research demonstrates that plasma metabolites play a pivotal role in the pathogenesis of CCA induced by gut microbiota. This finding deepens our understanding of how gut microbiota dysbiosis contributes to the development of CCA by influencing plasma metabolites. Cholangiocarcinoma gut microbiota metabolites mediator factors causal inference Mendelian randomization Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction 1.1 Background Cholangiocarcinoma (CCA), the second most common liver cancer type following hepatocellular carcinoma, accounts for 10–15% of all primary malignant liver tumors [ 1 ] . Over the past decade, both the incidence and mortality rates of CCA have been steadily increasing, with mortality rates exceeding 6/100,000 in some high-risk areas [ 1 ] . Arising from the epithelial cells of the bile ducts, CCA can be categorized as intrahepatic (iCCA) or extrahepatic (eCCA). As the disease progresses, tumor cells gradually invade and disrupt the normal structure of the bile ducts, leading to bile duct obstruction and bile stasis. Due to the often nonspecific early symptoms of cholangiocarcinoma, many patients are diagnosed in the advanced stages, complicating treatment [ 2 ] . Cholangiocarcinoma (CCA) exhibits a high degree of malignancy, characterized by a proclivity for infiltrating neighboring tissues and vasculature, facilitating lymphatic metastasis and distant dissemination. Consequently, patients diagnosed with CCA often face a dismal prognosis, marked by abbreviated survival periods. Presently, clinical management strategies for CCA are constrained, primarily relying on R0 surgical resection [ 3 ] . Hence, there exists an imperative to delve into the underlying pathogenesis of CCA and institute measures for early detection and prompt intervention, pivotal for mitigating mortality rates. With the continuous advancement of medical research, scientists are gradually recognizing the intricate correlation between the occurrence and progression of cholangiocarcinoma and various factors, such as hepatobiliary stones, viral hepatitis, and so forth [ 4 ] . Among these myriad factors, the disruption of intestinal microbiota balance has drawn considerable attention from researchers. As the largest ecosystem and immune organ in the human body, the intestines harbor trillions of microorganisms. The gut microbiota (GM), as a significant component of the human body, plays a vital role in regulating metabolism and immune function [ 5 ] . A wealth of research has demonstrated that dysbiosis of the GM can lead to the onset of various clinical diseases, such as psychiatric disorders, cardiovascular diseases, malignant tumors, and more [ 6 , 7 ] . Encouragingly, interventions aimed at adjusting the composition of the gut microbiota, such as fecal microbiota transplantation and targeted microbiota therapy, have been shown to effectively strengthen the intestinal barrier. Consequently, they represent a novel approach in clinical practice for the prevention and treatment of certain diseases [ 8 ] . Previous studies have clearly revealed the significant role of the gut microbiota in the formation of gastrointestinal malignancies. These microbial communities delay the onset of gastric and rectal cancers by influencing the digestion and absorption processes of proteins, as well as by degrading potential carcinogens [ 9 ] . Given the close association between the gastrointestinal tract and the biliary duct, both of which are directly involved in the digestion, absorption, and metabolism of food, it is reasonable to speculate that the role of intestinal microbiota in the gastrointestinal tract and biliary duct may share similarities. Earlier research has confirmed a close correlation between gut microbiota imbalance and the progression of non-alcoholic steatohepatitis (NASH) and cirrhosis, both of which are significant risk factors for cholangiocarcinoma (CCA). Further studies have also demonstrated a tight connection between gut microbiota and CCA [ 10 , 11 ] . However, the current literature has not thoroughly elucidated which specific microbial communities play a crucial role in the development of CCA, nor has it provided an in-depth description of the underlying pathogenic mechanisms. In summary, the causal relationship between gut microbiota and CCA, as well as the underlying mechanisms behind this relationship, remain unclear. It's intriguing that numerous clinical and animal studies have revealed a close association between the imbalance of plasma metabolites and the occurrence of cancer. For instance, elevated levels of pseudouridine in plasma are significantly associated with increased risk of ovarian cancer [ 12 ] . Similarly, abnormal L-tryptophan metabolism has been shown to drive the progression of breast, renal cell, and bladder cancers [ 13 ] . Furthermore, direct correlations have been found between arachidonic acid and high-density lipoprotein with increased risk of lung and breast cancers, respectively [ 14 , 15 ] . Conversely, the presence of 1-acyl-glycerophosphoethanolamine is associated with reduced risk of colorectal cancer [ 16 ] . These findings underscore the importance of plasma metabolites in the occurrence and progression of malignant tumors. Given that gut microbiota primarily influence host metabolism, it is plausible to speculate that the development of cholangiocarcinoma (CCA) may also be influenced by gut microbiota through plasma metabolites. However, the causal relationship between plasma metabolites and CCA remains relatively unexplored and requires further investigation. Therefore, we hypothesize the existence of a potential causal relationship between gut microbiota, plasma metabolites, and CCA. In this study, we aim to elucidate these relationships and identify plasma metabolite targets that may hold potential value for early diagnosis and clinical treatment of CCA. Through this research, we hope to provide new insights and methods for the prevention and treatment of CCA. 1.2 Objectives Mendelian randomization (MR) employs genetic variants from genome-wide association studies (GWAS) as instrumental variables (IVs) to infer the causal impact of environmental exposures on observed outcomes. Since an individual's genotype is established at conception and remains fixed throughout life, there is no possibility of reverse causation or confounding bias affecting the relationship between genotype and disease [ 17 ] . This unique characteristic of genetic makeup ensures that any observed associations between specific genetic variants and disease are less susceptible to being misconstrued or distorted by issues of causal inference from external factors [ 18 ] . In the current study, we conducted a bidirectional MR analysis and two mediation analyses, utilizing summary statistics data from the largest and most recent GWAS on gut microbiota, plasma metabolites, and cholangiocarcinoma to dissect their interrelations. Materials and Methods 2.1 Study design and data sources The summary statistics data for plasma metabolites GWAS were obtained from a human gut microbiome study conducted in individuals of European ancestry, comprising a total of 7,738 individuals [ 19 ] ( https://gwas.mrcieu.ac.uk ). Compared to the data obtained from MiBioGen, the gut microbiome data used in this study are the most recent. It is noteworthy that due to the substantial representation of individuals of European ancestry, this dataset is suitable for MR analysis in European populations. The summary statistics data for plasma metabolites GWAS were obtained from four human metabolomics studies conducted in individuals of European ancestry ( https://gwas.mrcieu.ac.uk ). A total of 452 plasma metabolites were analyzed, with specific data sources detailed in the following Table 1 [ 20 – 23 ] . The summary statistics data for cholangiocarcinoma GWAS primarily included study data from individuals of European ancestry, comprising 476,091 European individuals, including 832 cases of CCA and 475,259 controls [ 24 ] . All data sources are summarized in Table 1 . The article first successfully identified gut microbiota closely associated with cholangiocarcinoma (CCA) through bidirectional MR analysis. Subsequently, the same method was employed to screen plasma metabolites significantly associated with CCA. In order to further explore the potential causal relationship between these two sets of biomarkers, bidirectional Mendelian randomization (MR) analysis was conducted between the screened plasma metabolites and the previously identified gut microbiota. This analysis aimed to elucidate the interaction between gut microbiota and plasma metabolites in the occurrence and development of CCA, providing a more solid scientific basis for future prevention and treatment strategies. Table 1 Details of the genome-wide association studies and datasets used in our analyses. Exposure or outcome Sample size Number of SNPs Ancestry Author Gut microbiome [ 19 ] 7738 5563595 European Lopera-Maya EA Plasma Metabolites [ 20 ] 18960 11598671 European Johannes Kettunen Plasma Metabolites [ 21 ] 115078 12321875 European Borges CM Plasma Metabolites [ 22 ] 497 1623131 European Roederer Plasma Metabolites [ 23 ] 7697 2545584 European Shin Cholangiocarcinoma [ 24 ] 476091 24196592 European Sakaue S 2.2 Assumptions of MR We conducted a two-sample Mendelian randomization (MR) analysis using summary data from genome-wide association studies (GWAS), which relies on using genetic variation as instrumental variables (IVs) to infer the relationship between exposure and outcome. This method relies on three core assumptions: (1) IVs are closely associated with the exposure (relevance assumption); (2) IVs are not influenced by confounding factors that affect both the exposure and the outcome (independence assumption); (3) IVs only affect the outcome through the exposure under study, without any additional pathways (exclusion restriction assumption). Compared to traditional observational studies, this method can effectively overcome common confounding factors, leading to more reliable causal relationships and providing insights into causal temporality [ 25 ] . The flowchart of the study design is illustrated in Fig. 1 . 2.3 IV selection In the selection and screening of instrumental variables (IVs) for gut microbiota research, we adhered to a series of stringent criteria to ensure the integrity and reliability of the results. Given the scarcity of single nucleotide polymorphisms (SNPs) reaching statistical significance at the genome-wide level (p < 5 × 10 − 8), we adjusted the significance threshold to the genome-wide significant level (p < 1 × 10 − 5) based on relevant literature, consistent with similar standards employed in previously published studies [ 26 ] . To ensure the quality of instrumental variables, we implemented the following quality control steps: Firstly, to ensure the independence of each IV, we utilized methods from the 1,000 Genomes Project European cohort, setting the linkage disequilibrium threshold to R²<0.001 and the clumping distance to 10,000 kb using R packages, thereby reducing potential correlations between SNPs. Secondly, we excluded SNPs with minor allele frequency (MAF) ≤ 0.01, as these low-frequency variants may represent rare genetic variations, introducing controversy in genetic association analyses. Thirdly, we removed SNPs with palindromic structures, which may introduce unnecessary complexity during the analysis process. Lastly, to mitigate the risk of bias introduced by weak instrumental variables, we assessed the statistical strength of each SNP using the F-statistic (F = beta²/se²) [ 27 ] . SNPs with an F-value less than 10 were considered weak instrumental variables, potentially violating the first assumption of Mendelian randomization (MR) studies, and thus were excluded from further analysis. The specific data of all SNPs used in this experiment can be found in Supplementary Material 1. By adhering to these stringent criteria and quality control steps, we ensured the effectiveness of the selected instrumental variables, enabling a more accurate assessment of the potential associations between gut microbiota and phenotypes of interest. 2.4 MR analysis and sensitivity test In the Mendelian randomization (MR) analysis of gut microbiota and plasma metabolites in cholangiocarcinoma, we primarily relied on the Inverse Variance Weighted (IVW) method, which combines association estimates from multiple genetic variants, to assess potential causal relationships. Our significance threshold was set at p < 0.05, aiding in the identification of promising metabolites for further investigation while avoiding the potential omission of important findings due to overly stringent criteria [ 28 ] . While the IVW method is pivotal for its accuracy in estimating causal effects, it may introduce bias in the presence of pleiotropic variants. To address this challenge, we employed MR-Egger regression, an improved version of the IVW method. MR-Egger regression introduces an intercept term to estimate pleiotropy parameters and adjusts causal effect estimates accordingly, enhancing robustness against pleiotropy. A non-zero intercept indicates unbalanced pleiotropy. To comprehensively assess the results, we utilized Cochran’s Q statistic to analyze heterogeneity among variable-specific causal estimates and conducted leave-one-out analysis to examine the influence of specific genetic variants on causal effect estimates [ 29 , 30 ] . Results indicated p-values exceeding 0.05, suggesting no significant heterogeneity or horizontal pleiotropy. To delve into the direct and indirect impacts of gut microbiota (GM) and blood metabolites on cholangiocarcinoma (CCA), we employed a Two-Step Mendelian Randomization (TSMR) approach. The core assumption of TSMR is the absence of interaction between the exposure (GM) and the mediator (blood metabolites). Within the TSMR framework, apart from obtaining the overall effect estimate of GM on CCA (b0), we computed two additional crucial estimates: 1. the causal effect of exposure (GM) on the mediator (plasma metabolites) (b1); 2. the causal effect of the mediator (blood metabolites) on CCA (b2). Based on these two effect estimates, we further calculated: the mediating effect, represented by the product of b1 and b2 (b1*b2); the indirect effect, derived by subtracting the mediating effect from the total effect (b0-b1*b2); and the ratio of the mediating effect to the total effect ([b1*b2]/b0). 2.5 Statistical software All statistical analyses in this study were conducted using R software (version 4.2.1) with the following packages: "TwoSampleMR" (version 0.5.4), "ieugwasr" (version 0.1.5), "ggplot2" (version 3.4.3), "MendelianRandomization" (version 0.9.0), and "TSMR" (version 0.4.0). 2.6 Ethics statement The GWAS summary statistics data used in our study were publicly available, and we obtained informed consent from all participating studies by following the protocols approved by their respective institutional review boards. No separate ethical approval was required for this study. Results 3.1 Genetic causality and correlation between gut microbiota and CCA When evaluating the causal relationship between gut microbiota (GM) and cholangiocarcinoma (CCA), we used five methods: MR Egger, weighted median, inverse variance weighted (IVW), simple mode, and weighted mode, primarily focusing on the IVW method. The results showed that two types of microbiota were positively correlated with CCA, while eight types were negatively correlated with CCA. Lachnospiraceae (OR 0.599, 95% CI 0.366 to 0.983, P = 0.0425) and Eggerthella (OR 0.576, 95% CI 0.372 to 0.892, P = 0.0134) exhibited protective effects against CCA. Enterobacteriaceae (OR 2.603, 95% CI 1.205 to 5.620, P = 0.0149) and Enterobacteriales (OR 2.602, 95% CI 1.205 to 5.619, P = 0.0149) significantly promoted the incidence of CCA. Additionally, Acidaminococcaceae (OR 1.583, 95% CI 1.074 to 2.333, P = 0.0203), Gordonibacter (OR 1.369, 95% CI 1.076 to 1.743, P = 0.0106), Bifidobacterium adolescentis (OR 1.413, 95% CI 1.024 to 1.948, P = 0.0352), Bifidobacterium catenulatum (OR 1.420, 95% CI 1.045 to 1.928, P = 0.0248), Gordonibacter pamelaeae (OR 1.374, 95% CI 1.044 to 1.807, P = 0.0231), and Eubacterium eligens (OR 1.509, 95% CI 1.041 to 2.187, P = 0.0300) showed potential causal links to increased CCA risk. The heterogeneity test results indicated that the effects of the gut microbiota instrumental variable were consistent across different subgroups (P-value > 0.05), suggesting no significant effect heterogeneity in the study. The pleiotropy test results demonstrated that the gut microbiota instrumental variable did not significantly affect other potential confounding factors besides cholangiocarcinoma (P-value > 0.05), indicating a high specificity of the selected instrumental variable (Supplementary Material 2). Further reverse MR analysis of GM and CCA to evaluate the relationship of the aforementioned ten microbiota with CCA revealed no significant positive results (Supplementary Material 3). 3.2 Genetic causality and correlation between plasma metabolites and CCA When evaluating the causal relationship between plasma metabolites and cholangiocarcinoma (CCA), we used five methods: MR Egger, weighted median, inverse variance weighted (IVW), simple mode, and weighted mode, primarily focusing on the IVW method. The results showed that a total of 31 plasma metabolites had a causal relationship with CCA. Among them, Very Large Very Low-Density Lipoprotein Phospholipids (XXL_VLDL_PL; OR 1.340, 95% CI 1.043 to 1.723, P = 0.022), Intermediate-Density Lipoprotein Triglycerides (IDL_TG; OR 1.252, 95% CI 1.031 to 1.520, P = 0.023), Very Large Very Low-Density Lipoprotein Cholesterol (XXL_VLDL_C; OR 1.356, 95% CI 1.033 to 1.779, P = 0.028), Extra Small Very Low-Density Lipoprotein Triglycerides (XS_VLDL_TG; OR 1.264, 95% CI 1.018 to 1.569, P = 0.034), and Large High-Density Lipoprotein Phospholipids (L_HDL_PL; OR 1.287, 95% CI 1.011 to 1.638, P = 0.040) were positively correlated with CCA, while the remaining 26 plasma metabolites were negatively correlated with CCA (see Fig. 3 for details). The heterogeneity test results indicated that the effects of the plasma metabolites instrumental variable were consistent across different subgroups (P-value > 0.05), suggesting no significant effect heterogeneity in the study. The horizontal pleiotropy test results showed that the plasma metabolites instrumental variable did not significantly affect other potential confounding factors besides cholangiocarcinoma (P-value > 0.05), indicating high specificity of the selected instrumental variable (Supplementary Material 4). Similarly, we conducted a reverse MR analysis of plasma metabolites and CCA, but no significant positive results were observed (Supplementary Material 5). 3.3 Mediation analyses of potential blood metabolites We conducted a bidirectional Mendelian randomization (MR) analysis between 10 types of gut microbiota identified in the first screening step and 31 plasma metabolites identified in the second screening step. The study results (Supplementary Material 3) showed that 4 types of gut microbiota could cause changes in CCA by acting on 10 plasma metabolites (Supplementary Material 6). This indicates that plasma metabolites can serve as mediators in the causal relationship between gut microbiota and CCA. In Fig. 4 , Intermediate-Density Lipoprotein Triglycerides (IDL_TG; OR 1.252, 95% CI 1.031 to 1.520, P = 0.023) is positively correlated with CCA, while the remaining plasma metabolites are negatively correlated with the risk of developing CCA. We calculated the indirect effects and proportions mediated by these metabolites, finding that the effects of L_LDL_FC and IDL_TG remained significant for f_Enterobacteriaceae and o_Enterobacteriales after adjusting for gut microbiota. Similarly, in s_Bifidobacterium_adolescentis, the effect of XS_VLDL_CE persisted; and in s_Eubacterium_eligens, XXL_VLDL_C also played a role. Overall, we observed indirect effects of L_LDL_FC and IDL_TG in the association between f_Enterobacteriaceae and o_Enterobacteriales and CCA, with mediation proportions of 2.2% (P = 0.038) and 1.4% (P = 0.032), respectively; and mediation proportions of XS_VLDL_CE and XXL_VLDL_C between s_Bifidobacterium_adolescentis, s_Eubacterium_eligens, and CCA were 3.5% (P = 0.006) and 3.0% (P = 0.025), respectively. Discussion Cholangiocarcinoma (CCA) is a malignant tumor originating from the epithelial cells of the bile ducts, with an increasing incidence worldwide [ 31 , 32 ] . Although CCA accounts for only 15% of primary liver malignancies, most cases are diagnosed at advanced stages with poor prognosis. Only 30% of tumors are curable at the time of diagnosis, and the five-year survival rate ranges from 7–20%, posing a significant threat to public health [ 33 , 34 ] . The human gut hosts a complex microbial community, including bacteria, fungi, archaea, viruses, and protozoa, which play a crucial role in maintaining human health [ 35 ] . The gut microbiota coexists with the intestinal mucosa, providing essential immune, metabolic, and gastrointestinal protective functions for healthy individuals [ 36 ] . A reduction in microbial diversity may increase susceptibility to various diseases, including cancer [ 37 , 38 ] . In recent years, extensive research has been conducted on the role of gut microbiota in the occurrence and development of CCA and its impact on diagnostic and therapeutic strategies [ 39 – 41 ] . For example, studies have shown that an increase in genetically predicted Veillonellaceae, Alistipes, Enterobacteriaceae, and Firmicutes is associated with a higher risk of intrahepatic cholangiocarcinoma (ICC), while an increase in anaerobic bacteria, Paraprevotella, Parasutterella, and Verrucomicrobia appears to be protective. Bioinformatics analyses have indicated that differentially expressed genes near gut microbiome-associated loci may influence ICC through regulatory pathways and the tumor immune microenvironment [ 41 , 42 ] . These findings suggest an important role of the gut microbiome in CCA occurrence and development, but comprehensive causal relationship analyses between gut microbiota and CCA are still lacking in the literature. Plasma metabolites play a crucial role in energy metabolism, signal transduction, and immune regulation in the body. Metabolic disturbances are closely related to the occurrence and development of various diseases, including cancer [ 43 , 44 ] . Plasma metabolites are small molecules widely present in the blood, including amino acids, lipids, sugars, and organic acids. These metabolites maintain normal physiological functions and metabolic balance by participating in various biochemical reactions and metabolic pathways. For example, studies have shown significant associations between different plasma metabolites and cancer risk. Circulating metabolites such as O-methyl catechol sulfate and 4-ethylphenyl sulfate are significantly associated with the risk of lung cancer and renal cell carcinoma, respectively [ 44 ] . These studies suggest that plasma metabolites may play an important role in cancer occurrence and development. Therefore, our study is the first to use summary statistics from genome-wide association studies (GWAS) to conduct two-sample MR and TSMR analyses to explore the potential causal links between gut microbiota, plasma metabolites, and CCA. Through this analytical approach, we aim to provide new insights for effective prevention and intervention strategies for CCA and to gain a deeper understanding of the pathogenesis of CCA from the perspective of the gut microbiome. In the causal relationship analysis between the gut microbiome and CCA, we found that two microbes were positively correlated with CCA, while eight microbes were negatively correlated. Specifically, Enterobacteriaceae and Enterobacteriales significantly increased the risk of CCA, which aligns with previous research linking gut pathogens to tumor development. For instance, some pathogenic bacteria in the gut microbiome can induce chronic inflammation and cellular proliferation of the intestinal mucosa by producing toxins and metabolic products, thereby increasing the risk of tumor development. Conversely, Lachnospiraceae and Eggerthella showed protective effects against CCA, suggesting that these microbiota might reduce cancer risk by inhibiting inflammatory responses or modulating immune function. These findings not only deepen our understanding of the role of the gut microbiome in cancer development but also provide new ideas for future prevention or treatment of CCA by regulating the gut microbiota. We further analyzed the causal relationship between plasma metabolites and CCA and found that 31 metabolites were significantly associated with CCA. Among them, very low-density lipoprotein phospholipids (XXL_VLDL_PL), intermediate-density lipoprotein triglycerides (IDL_TG), very low-density lipoprotein cholesterol (XXL_VLDL_C), very small very low-density lipoprotein triglycerides (XS_VLDL_TG), and large high-density lipoprotein phospholipids (L_HDL_PL) were positively correlated with CCA. These results indicate that lipid metabolism disorders may be an important risk factor for the development of CCA. In particular, lipoprotein-associated metabolites may play a key role in the pathophysiology of cancer. Lipoprotein metabolism disorders in the blood may lead to hepatic steatosis and inflammatory responses, thereby promoting the development of liver cancer. Therefore, interventions targeting these metabolites may provide new pathways for the prevention and treatment of CCA. Through Two-Step MR (TSMR) analysis, we found that plasma metabolites mediate the relationship between the gut microbiome and CCA. Specifically, Intermediate-Density Lipoprotein Triglycerides (IDL_TG) significantly mediated the association between f_Enterobacteriaceae and o_Enterobacteriales and CCA. This suggests that these plasma metabolites may influence the risk of CCA by affecting the metabolic function of gut microbiota. Additionally, XS_VLDL_CE and XXL_VLDL_C significantly mediated the association between s_Bifidobacterium_adolescentis and s_Eubacterium_eligens and CCA, respectively. These findings further support the hypothesis that plasma metabolites act as mediators of the gut microbiota's impact on cancer risk and provide clear directions for future research, such as exploring how to intervene in the gut microbiota by regulating plasma metabolites to reduce the risk of CCA. This study is the first to systematically reveal the causal relationships and complex interactions between the gut microbiome and plasma metabolites and CCA. This not only enriches our understanding of the pathogenesis of CCA but also provides new potential targets for future prevention and treatment strategies. Specifically, regulating the metabolic pathways of the gut microbiota and plasma metabolites may achieve early intervention and precision treatment of CCA. However, the study has certain limitations, such as limited sample size and variability among different populations. Future research should further validate these findings and explore their clinical application value. Overall, this study provides new insights into the role of the gut microbiome and plasma metabolites in CCA occurrence, suggesting that comprehensive regulation of these biomarkers may achieve effective prevention and control of CCA. Future research should continue to explore these mechanisms and develop targeted interventions to improve the prognosis of CCA patients. Conclusion To our knowledge, this is the first comprehensive study to evaluate the causal relationships between gut microbiota, plasma metabolites, and cholangiocarcinoma (CCA). These findings underscore the importance of uncovering the potential mechanisms linking gut microbiota and CCA. The results of this study provide new insights into the application of microbiome-based therapies and metabolite-targeted interventions in CCA. By further exploring these mechanisms, it is hoped that more effective prevention and treatment strategies can be developed to improve the prognosis of CCA patients. Declarations Funding statement This work was supported by the National Natural Science Foundation of China (grant number 82003073, www.nsfc.gov.cn), the Guangdong Science and Technology Department (grant number 2020A1515011296, http://pro.gdstc.gov.cn) and the National Natural Science Foundation of China (No. 81971463). Conflicts of interest The authors have no financial conflicts of interest. Data availability statement The original contributions presented in the study are included in the article/Supplementary material, further inquiries can be directed to the corresponding author. Author contribution Conceptualization: SX, XH ,LL. Study design: SX, HY, JR, QZ, WL. Methodology: SX, LL, JR. Data collection: all authors. Investigation: all authors. Statistical analysis: SX, XH, QZ. Writing—original draft: SX, XH, LL. Writing—review & editing: all authors. Funding acquisition: HY, WL. Approval of final manuscript: all authors. 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A cross-population atlas of genetic associations for 220 human phenotypes[J]. Nature Genetics, 2021, 53(10): 1415-1424. Sekula P, Del Greco M F, Pattaro C, et al. Mendelian Randomization as an Approach to Assess Causality Using Observational Data[J]. Journal of the American Society of Nephrology: JASN, 2016, 27(11): 3253-3265. Sanna S, van Zuydam N R, Mahajan A, et al. Causal relationships among the gut microbiome, short-chain fatty acids and metabolic diseases[J]. Nature Genetics, 2019, 51(4): 600-605. Burgess S, Thompson S G, CRP CHD Genetics Collaboration. Avoiding bias from weak instruments in Mendelian randomization studies[J]. International Journal of Epidemiology, 2011, 40(3): 755-764. Burgess S, Butterworth A, Thompson S G. Mendelian randomization analysis with multiple genetic variants using summarized data[J]. Genetic Epidemiology, 2013, 37(7): 658-665. Hemani G, Bowden J, Davey Smith G. Evaluating the potential role of pleiotropy in Mendelian randomization studies[J]. 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HPB, 2018, 20(1): 83-92. Amoroso C, Perillo F, Strati F, et al. The Role of Gut Microbiota Biomodulators on Mucosal Immunity and Intestinal Inflammation[J]. Cells, 2020, 9(5): 1234. Chen Y, Zhou J, Wang L. Role and Mechanism of Gut Microbiota in Human Disease[J]. Frontiers in Cellular and Infection Microbiology, 2021, 11: 625913. Gong B, Wang C, Meng F, et al. Association Between Gut Microbiota and Autoimmune Thyroid Disease: A Systematic Review and Meta-Analysis[J]. Frontiers in Endocrinology, 2021, 12: 774362. Lee Y K, Mazmanian S K. Has the microbiota played a critical role in the evolution of the adaptive immune system?[J]. Science (New York, N.Y.), 2010, 330(6012): 1768-1773. Zhang Q, Zhou J, Zhai D, et al. Gut microbiota regulates the ALK5/NOX1 axis by altering glutamine metabolism to inhibit ferroptosis of intrahepatic cholangiocarcinoma cells[J]. Biochimica Et Biophysica Acta. Molecular Basis of Disease, 2024, 1870(5): 167152. Wheatley R C, Kilgour E, Jacobs T, et al. Potential influence of the microbiome environment in patients with biliary tract cancer and implications for therapy[J]. British Journal of Cancer, 2022, 126(5): 693-705. Chen Z, Shi W, Chen K, et al. Elucidating the causal association between gut microbiota and intrahepatic cholangiocarcinoma through Mendelian randomization analysis[J]. Frontiers in Microbiology, 2023, 14: 1288525. Wang J, Zhou X, Li X, et al. Fecal Microbiota Transplantation Alters the Outcome of Hepatitis B Virus Infection in Mice[J]. Frontiers in Cellular and Infection Microbiology, 2022, 12: 844132. Vidman L, Zheng R, Bodén S, et al. Untargeted plasma metabolomics and risk of colorectal cancer-an analysis nested within a large-scale prospective cohort[J]. Cancer & Metabolism, 2023, 11(1): 17. Chen Y, Xie Y, Ci H, et al. Plasma metabolites and risk of seven cancers: a two-sample Mendelian randomization study among European descendants[J]. BMC medicine, 2024, 22(1): 90. Table 2 Table 2 is available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files Table2.docx SupplementaryMaterial1.7z SupplementaryMaterial2.7z SupplementaryMaterial3.7z SupplementaryMaterial4.7z SupplementaryMaterial5.7z SupplementaryMaterial6.7z Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-4687408","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":333244999,"identity":"289b7d34-5469-4f38-ae6e-577ef818a8cb","order_by":0,"name":"Sicheng Xu","email":"","orcid":"","institution":"Sun Yat-sen Memorial Hospital, Sun Yat-sen University","correspondingAuthor":false,"prefix":"","firstName":"Sicheng","middleName":"","lastName":"Xu","suffix":""},{"id":333245001,"identity":"b1222a84-c2b9-400f-b65a-c21f90b1ee9d","order_by":1,"name":"Xing He","email":"","orcid":"","institution":"Sun Yat-sen Memorial Hospital, Sun Yat-sen University","correspondingAuthor":false,"prefix":"","firstName":"Xing","middleName":"","lastName":"He","suffix":""},{"id":333245002,"identity":"1ed11dd2-5368-4776-94d6-c4c338afa044","order_by":2,"name":"Liqiang Liu","email":"","orcid":"","institution":"Sun Yat-sen Memorial Hospital, Sun Yat-sen University","correspondingAuthor":false,"prefix":"","firstName":"Liqiang","middleName":"","lastName":"Liu","suffix":""},{"id":333245004,"identity":"30eaac2a-3352-4a54-bccc-08a96154cdc8","order_by":3,"name":"Junkai Ren","email":"","orcid":"","institution":"Sun Yat-sen Memorial Hospital, Sun Yat-sen University","correspondingAuthor":false,"prefix":"","firstName":"Junkai","middleName":"","lastName":"Ren","suffix":""},{"id":333245007,"identity":"4e94c563-18ed-401a-9559-10cc3299de89","order_by":4,"name":"Qixian Zhou","email":"","orcid":"","institution":"Sun Yat-sen Memorial Hospital, Sun Yat-sen University","correspondingAuthor":false,"prefix":"","firstName":"Qixian","middleName":"","lastName":"Zhou","suffix":""},{"id":333245008,"identity":"a2a93f84-34f0-4b1e-bf19-6c7823bc9765","order_by":5,"name":"Huilin Ye","email":"","orcid":"","institution":"Sun Yat-sen Memorial Hospital, Sun Yat-sen University","correspondingAuthor":false,"prefix":"","firstName":"Huilin","middleName":"","lastName":"Ye","suffix":""},{"id":333245012,"identity":"975cbb4f-a7cb-405b-b2b1-fef4f74deee2","order_by":6,"name":"Wenbin Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBUlEQVRIiWNgGAWjYBACPmYgwdggwcAPpA88gAnz4NHCBtMi2QDUkkCUFgawFgYGgwNABnFa2HkMHxfusMgzvnb4IdCWusT5MxIYH7xtY5A3x+kwHmPjmWckis1upxkAtRxO3HAjgdlwbhuD4c4GXFp4t0nztkkkbrudANJyIHGDRAIbUIQhAexU7Fq2/wZp2Tw7/QPMYey/CWjZxgzSskE6B2QLc2LDjQSgIF4t/J+lec9IJM64nVNwIMHgsPGGMw+bJeeckzDcgEMLP/+xxM+8O+oS+2enb/7woaJOdn578sEPb8ps5HHZggYMGBwbwNHEIEGUejCwJ17pKBgFo2AUjBQAADF/WWP7a2s7AAAAAElFTkSuQmCC","orcid":"","institution":"Sun Yat-sen Memorial Hospital, Sun Yat-sen University","correspondingAuthor":true,"prefix":"","firstName":"Wenbin","middleName":"","lastName":"Li","suffix":""},{"id":333245013,"identity":"77782310-1135-4a52-9f25-233022b5aa06","order_by":7,"name":"Haikuo Wang","email":"","orcid":"","institution":"Chinese Academy of Medical Sciences \u0026 Peking Union Medical College","correspondingAuthor":false,"prefix":"","firstName":"Haikuo","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2024-07-04 14:56:30","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4687408/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4687408/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":62185584,"identity":"543f0514-4295-4a7d-9a16-ab5986c89a9d","added_by":"auto","created_at":"2024-08-10 11:54:51","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":400054,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAssumptions and design of the bidirectional and mediation Mendelian randomization (MR) analyses. Firstly, a two-sample bidirectional MR was performed to investigate the causal relationships between gut microbiota (exposure) and cholangiocarcinoma (outcome). Secondly, 31 plasma metabolites (mediator) were selected for subsequent mediation analyses. Finally, a two-step MR analysis was conducted to detect potential mediating metabolites (Step 1, the effect of gut microbiota on metabolites; Step 2, the effect of metabolites on cholangiocarcinoma), followed by a validation analysis using two-sample MR.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4687408/v1/9ca2e5dcba95fe3eef35d405.png"},{"id":62186119,"identity":"f109042c-ae70-4041-82ff-b290a0de2e35","added_by":"auto","created_at":"2024-08-10 12:02:51","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":48459,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA forest plot depicts the associations between genetically predicted increases in 10 bacterial taxa and cholangiocarcinoma (CCA) risk. CI, confidence interval; OR, odds ratio; SNP, single nucleotide polymorphism.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-4687408/v1/9ae1abe44e4273ddfd9c244d.png"},{"id":62185590,"identity":"5c660d43-3ac8-4bab-a5cf-766e4b044c65","added_by":"auto","created_at":"2024-08-10 11:54:52","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":122576,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA forest plot depicts the associations between genetically predicted increases in 31 plasma metabolites and cholangiocarcinoma (CCA) risk.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-4687408/v1/bbc15c51b8a3fbd714613dbb.png"},{"id":62186122,"identity":"eb0dbb38-8d53-4822-9750-25863e3efaa3","added_by":"auto","created_at":"2024-08-10 12:02:52","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":183284,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMendelian randomization analyses show causal effects of plasma metabolites on gut microbiota and CCA. The diagram displays the mediation mode of “gut microbiota-plasma metabolites-cholangiocarcinoma” in two-step Mendelian randomization. Beta values (β) indicate the causal effect estimates using the inverse-variance-weighted method (truncated at P\u0026lt;0.05).Different colors represent different types of bacterial communities.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-4687408/v1/63e315d5f4d4647af6a15cd8.png"},{"id":63057119,"identity":"74cf4853-db39-4b67-b98e-c29f3a1761d3","added_by":"auto","created_at":"2024-08-22 15:22:37","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":7609278,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4687408/v1/09834602-d9a4-478e-a4a6-1c5fa80e9768.pdf"},{"id":62185582,"identity":"85e1330e-7f44-430c-a159-2f78e6eed7f4","added_by":"auto","created_at":"2024-08-10 11:54:51","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":78831,"visible":true,"origin":"","legend":"","description":"","filename":"Table2.docx","url":"https://assets-eu.researchsquare.com/files/rs-4687408/v1/83195a06059ebefcfc68d4ac.docx"},{"id":62185591,"identity":"665edd94-608c-49f0-9bd7-0e1ad0cffcc9","added_by":"auto","created_at":"2024-08-10 11:54:52","extension":"7z","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":718311,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial1.7z","url":"https://assets-eu.researchsquare.com/files/rs-4687408/v1/9f35e68c6b54f0364e607822.7z"},{"id":62185585,"identity":"135c88ab-c716-4b9d-b8c3-29efefc5ab19","added_by":"auto","created_at":"2024-08-10 11:54:52","extension":"7z","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":70158,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial2.7z","url":"https://assets-eu.researchsquare.com/files/rs-4687408/v1/695c9dd97e06d4880926414a.7z"},{"id":62185586,"identity":"ff22402a-1a40-414c-a0ad-91f2dff3cb6e","added_by":"auto","created_at":"2024-08-10 11:54:52","extension":"7z","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":3769,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial3.7z","url":"https://assets-eu.researchsquare.com/files/rs-4687408/v1/afe75e737a83b4e35c5ec276.7z"},{"id":62186120,"identity":"6dfa0b91-c3c1-4ae4-8c59-a3e7d6a570a3","added_by":"auto","created_at":"2024-08-10 12:02:52","extension":"7z","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":79305,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial4.7z","url":"https://assets-eu.researchsquare.com/files/rs-4687408/v1/60a59c37e4740a066fabb11e.7z"},{"id":62185588,"identity":"8b813cbf-c451-46c3-9e8b-02242dc9c650","added_by":"auto","created_at":"2024-08-10 11:54:52","extension":"7z","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":9354,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial5.7z","url":"https://assets-eu.researchsquare.com/files/rs-4687408/v1/ab850da20d234c151b6ced0f.7z"},{"id":62186121,"identity":"c7a857ad-c3cc-4ff9-9c97-de04ef35a38c","added_by":"auto","created_at":"2024-08-10 12:02:52","extension":"7z","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":138762,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial6.7z","url":"https://assets-eu.researchsquare.com/files/rs-4687408/v1/3750ea5a6cb55fded9a468c3.7z"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eCausal Relationship Among Intestinal Microbiota, Lipid Metabolites, and Cholangiocarcinoma: A Mendelian Randomization Study\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\n\u003ch3\u003e1.1 Background\u003c/h3\u003e\n\u003cp\u003e \u003cb\u003eCholangiocarcinoma (CCA), the second most common liver cancer type following hepatocellular carcinoma, accounts for 10\u0026ndash;15% of all primary malignant liver tumors\u003c/b\u003e \u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. \u003cb\u003eOver the past decade, both the incidence and mortality rates of CCA have been steadily increasing, with mortality rates exceeding 6/100,000 in some high-risk areas\u003c/b\u003e \u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. \u003cb\u003eArising from the epithelial cells of the bile ducts, CCA can be categorized as intrahepatic (iCCA) or extrahepatic (eCCA). As the disease progresses, tumor cells gradually invade and disrupt the normal structure of the bile ducts, leading to bile duct obstruction and bile stasis. Due to the often nonspecific early symptoms of cholangiocarcinoma, many patients are diagnosed in the advanced stages, complicating treatment\u003c/b\u003e \u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. \u003cb\u003eCholangiocarcinoma (CCA) exhibits a high degree of malignancy, characterized by a proclivity for infiltrating neighboring tissues and vasculature, facilitating lymphatic metastasis and distant dissemination. Consequently, patients diagnosed with CCA often face a dismal prognosis, marked by abbreviated survival periods. Presently, clinical management strategies for CCA are constrained, primarily relying on R0 surgical resection\u003c/b\u003e\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. \u003cb\u003eHence, there exists an imperative to delve into the underlying pathogenesis of CCA and institute measures for early detection and prompt intervention, pivotal for mitigating mortality rates.\u003c/b\u003e\u003c/p\u003e \u003cp\u003e \u003cb\u003eWith the continuous advancement of medical research, scientists are gradually recognizing the intricate correlation between the occurrence and progression of cholangiocarcinoma and various factors, such as hepatobiliary stones, viral hepatitis, and so forth\u003c/b\u003e \u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. \u003cb\u003eAmong these myriad factors, the disruption of intestinal microbiota balance has drawn considerable attention from researchers.\u003c/b\u003e\u003c/p\u003e \u003cp\u003e \u003cb\u003eAs the largest ecosystem and immune organ in the human body, the intestines harbor trillions of microorganisms. The gut microbiota (GM), as a significant component of the human body, plays a vital role in regulating metabolism and immune function\u003c/b\u003e \u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. \u003cb\u003eA wealth of research has demonstrated that dysbiosis of the GM can lead to the onset of various clinical diseases, such as psychiatric disorders, cardiovascular diseases, malignant tumors, and more\u003c/b\u003e \u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. \u003cb\u003eEncouragingly, interventions aimed at adjusting the composition of the gut microbiota, such as fecal microbiota transplantation and targeted microbiota therapy, have been shown to effectively strengthen the intestinal barrier. Consequently, they represent a novel approach in clinical practice for the prevention and treatment of certain diseases\u003c/b\u003e \u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003cb\u003ePrevious studies have clearly revealed the significant role of the gut microbiota in the formation of gastrointestinal malignancies. These microbial communities delay the onset of gastric and rectal cancers by influencing the digestion and absorption processes of proteins, as well as by degrading potential carcinogens\u003c/b\u003e \u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. \u003cb\u003eGiven the close association between the gastrointestinal tract and the biliary duct, both of which are directly involved in the digestion, absorption, and metabolism of food, it is reasonable to speculate that the role of intestinal microbiota in the gastrointestinal tract and biliary duct may share similarities.\u003c/b\u003e\u003c/p\u003e \u003cp\u003e \u003cb\u003eEarlier research has confirmed a close correlation between gut microbiota imbalance and the progression of non-alcoholic steatohepatitis (NASH) and cirrhosis, both of which are significant risk factors for cholangiocarcinoma (CCA). Further studies have also demonstrated a tight connection between gut microbiota and CCA\u003c/b\u003e \u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. \u003cb\u003eHowever, the current literature has not thoroughly elucidated which specific microbial communities play a crucial role in the development of CCA, nor has it provided an in-depth description of the underlying pathogenic mechanisms. In summary, the causal relationship between gut microbiota and CCA, as well as the underlying mechanisms behind this relationship, remain unclear.\u003c/b\u003e\u003c/p\u003e \u003cp\u003e \u003cb\u003eIt's intriguing that numerous clinical and animal studies have revealed a close association between the imbalance of plasma metabolites and the occurrence of cancer. For instance, elevated levels of pseudouridine in plasma are significantly associated with increased risk of ovarian cancer\u003c/b\u003e \u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. \u003cb\u003eSimilarly, abnormal L-tryptophan metabolism has been shown to drive the progression of breast, renal cell, and bladder cancers\u003c/b\u003e \u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. \u003cb\u003eFurthermore, direct correlations have been found between arachidonic acid and high-density lipoprotein with increased risk of lung and breast cancers, respectively\u003c/b\u003e \u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. \u003cb\u003eConversely, the presence of 1-acyl-glycerophosphoethanolamine is associated with reduced risk of colorectal cancer\u003c/b\u003e \u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003cb\u003eThese findings underscore the importance of plasma metabolites in the occurrence and progression of malignant tumors. Given that gut microbiota primarily influence host metabolism, it is plausible to speculate that the development of cholangiocarcinoma (CCA) may also be influenced by gut microbiota through plasma metabolites. However, the causal relationship between plasma metabolites and CCA remains relatively unexplored and requires further investigation.\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eTherefore, we hypothesize the existence of a potential causal relationship between gut microbiota, plasma metabolites, and CCA. In this study, we aim to elucidate these relationships and identify plasma metabolite targets that may hold potential value for early diagnosis and clinical treatment of CCA. Through this research, we hope to provide new insights and methods for the prevention and treatment of CCA.\u003c/b\u003e \u003c/p\u003e \u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003e1.2 Objectives\u003c/h2\u003e \u003cp\u003e \u003cb\u003eMendelian randomization (MR) employs genetic variants from genome-wide association studies (GWAS) as instrumental variables (IVs) to infer the causal impact of environmental exposures on observed outcomes. Since an individual's genotype is established at conception and remains fixed throughout life, there is no possibility of reverse causation or confounding bias affecting the relationship between genotype and disease\u003c/b\u003e \u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e. \u003cb\u003eThis unique characteristic of genetic makeup ensures that any observed associations between specific genetic variants and disease are less susceptible to being misconstrued or distorted by issues of causal inference from external factors\u003c/b\u003e \u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. \u003cb\u003eIn the current study, we conducted a bidirectional MR analysis and two mediation analyses, utilizing summary statistics data from the largest and most recent GWAS on gut microbiota, plasma metabolites, and cholangiocarcinoma to dissect their interrelations.\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study design and data sources\u003c/h2\u003e \u003cp\u003e \u003cb\u003eThe summary statistics data for plasma metabolites GWAS were obtained from a human gut microbiome study conducted in individuals of European ancestry, comprising a total of 7,738 individuals\u003c/b\u003e \u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e \u003cb\u003e(\u003c/b\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\u003cb\u003e). Compared to the data obtained from MiBioGen, the gut microbiome data used in this study are the most recent. It is noteworthy that due to the substantial representation of individuals of European ancestry, this dataset is suitable for MR analysis in European populations.\u003c/b\u003e\u003c/p\u003e \u003cp\u003e \u003cb\u003eThe summary statistics data for plasma metabolites GWAS were obtained from four human metabolomics studies conducted in individuals of European ancestry (\u003c/b\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 \u003cb\u003e). A total of 452 plasma metabolites were analyzed, with specific data sources detailed in the following\u003c/b\u003e Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003csup\u003e[\u003cspan additionalcitationids=\"CR21 CR22\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e.\u003cb\u003eThe summary statistics data for cholangiocarcinoma GWAS primarily included study data from individuals of European ancestry, comprising 476,091 European individuals, including 832 cases of CCA and 475,259 controls\u003c/b\u003e\u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e. \u003cb\u003eAll data sources are summarized in\u003c/b\u003e Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cb\u003eThe article first successfully identified gut microbiota closely associated with cholangiocarcinoma (CCA) through bidirectional MR analysis. Subsequently, the same method was employed to screen plasma metabolites significantly associated with CCA. In order to further explore the potential causal relationship between these two sets of biomarkers, bidirectional Mendelian randomization (MR) analysis was conducted between the screened plasma metabolites and the previously identified gut microbiota. This analysis aimed to elucidate the interaction between gut microbiota and plasma metabolites in the occurrence and development of CCA, providing a more solid scientific basis for future prevention and treatment strategies.\u003c/b\u003e \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\u003eDetails of the genome-wide association studies and datasets used in our analyses.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExposure or outcome\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSample size\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eNumber of SNPs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAncestry\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAuthor\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGut microbiome\u003c/b\u003e\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e7738\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e5563595\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e\u003cb\u003eEuropean\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eLopera-Maya EA\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePlasma Metabolites\u003c/b\u003e\u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e18960\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e11598671\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e\u003cb\u003eEuropean\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eJohannes Kettunen\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePlasma Metabolites\u003c/b\u003e\u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e115078\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e12321875\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e\u003cb\u003eEuropean\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eBorges CM\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePlasma Metabolites\u003c/b\u003e\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e497\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1623131\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e\u003cb\u003eEuropean\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eRoederer\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePlasma Metabolites\u003c/b\u003e\u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e7697\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e2545584\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e\u003cb\u003eEuropean\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eShin\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCholangiocarcinoma\u003c/b\u003e\u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e476091\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e24196592\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e\u003cb\u003eEuropean\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eSakaue S\u003c/b\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 \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Assumptions of MR\u003c/h2\u003e \u003cp\u003e \u003cb\u003eWe conducted a two-sample Mendelian randomization (MR) analysis using summary data from genome-wide association studies (GWAS), which relies on using genetic variation as instrumental variables (IVs) to infer the relationship between exposure and outcome. This method relies on three core assumptions: (1) IVs are closely associated with the exposure (relevance assumption); (2) IVs are not influenced by confounding factors that affect both the exposure and the outcome (independence assumption); (3) IVs only affect the outcome through the exposure under study, without any additional pathways (exclusion restriction assumption). Compared to traditional observational studies, this method can effectively overcome common confounding factors, leading to more reliable causal relationships and providing insights into causal temporality\u003c/b\u003e \u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e.\u003cb\u003eThe flowchart of the study design is illustrated in\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.3 IV selection\u003c/h2\u003e \u003cp\u003e \u003cb\u003eIn the selection and screening of instrumental variables (IVs) for gut microbiota research, we adhered to a series of stringent criteria to ensure the integrity and reliability of the results. Given the scarcity of single nucleotide polymorphisms (SNPs) reaching statistical significance at the genome-wide level (p\u0026thinsp;\u0026lt;\u0026thinsp;5 \u0026times; 10\u0026thinsp;\u0026minus;\u0026thinsp;8), we adjusted the significance threshold to the genome-wide significant level (p\u0026thinsp;\u0026lt;\u0026thinsp;1 \u0026times; 10\u0026thinsp;\u0026minus;\u0026thinsp;5) based on relevant literature, consistent with similar standards employed in previously published studies\u003c/b\u003e \u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e. \u003cb\u003eTo ensure the quality of instrumental variables, we implemented the following quality control steps: Firstly, to ensure the independence of each IV, we utilized methods from the 1,000 Genomes Project European cohort, setting the linkage disequilibrium threshold to R\u0026sup2;\u0026lt;0.001 and the clumping distance to 10,000 kb using R packages, thereby reducing potential correlations between SNPs. Secondly, we excluded SNPs with minor allele frequency (MAF)\u0026thinsp;\u0026le;\u0026thinsp;0.01, as these low-frequency variants may represent rare genetic variations, introducing controversy in genetic association analyses. Thirdly, we removed SNPs with palindromic structures, which may introduce unnecessary complexity during the analysis process. Lastly, to mitigate the risk of bias introduced by weak instrumental variables, we assessed the statistical strength of each SNP using the F-statistic (F\u0026thinsp;=\u0026thinsp;beta\u0026sup2;/se\u0026sup2;)\u003c/b\u003e\u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e. \u003cb\u003eSNPs with an F-value less than 10 were considered weak instrumental variables, potentially violating the first assumption of Mendelian randomization (MR) studies, and thus were excluded from further analysis. The specific data of all SNPs used in this experiment can be found in Supplementary Material 1. By adhering to these stringent criteria and quality control steps, we ensured the effectiveness of the selected instrumental variables, enabling a more accurate assessment of the potential associations between gut microbiota and phenotypes of interest.\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.4 MR analysis and sensitivity test\u003c/h2\u003e \u003cp\u003e \u003cb\u003eIn the Mendelian randomization (MR) analysis of gut microbiota and plasma metabolites in cholangiocarcinoma, we primarily relied on the Inverse Variance Weighted (IVW) method, which combines association estimates from multiple genetic variants, to assess potential causal relationships. Our significance threshold was set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, aiding in the identification of promising metabolites for further investigation while avoiding the potential omission of important findings due to overly stringent criteria\u003c/b\u003e \u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e.\u003cb\u003eWhile the IVW method is pivotal for its accuracy in estimating causal effects, it may introduce bias in the presence of pleiotropic variants. To address this challenge, we employed MR-Egger regression, an improved version of the IVW method. MR-Egger regression introduces an intercept term to estimate pleiotropy parameters and adjusts causal effect estimates accordingly, enhancing robustness against pleiotropy. A non-zero intercept indicates unbalanced pleiotropy. To comprehensively assess the results, we utilized Cochran\u0026rsquo;s Q statistic to analyze heterogeneity among variable-specific causal estimates and conducted leave-one-out analysis to examine the influence of specific genetic variants on causal effect estimates\u003c/b\u003e\u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e. \u003cb\u003eResults indicated p-values exceeding 0.05, suggesting no significant heterogeneity or horizontal pleiotropy.\u003c/b\u003e\u003c/p\u003e \u003cp\u003e \u003cb\u003eTo delve into the direct and indirect impacts of gut microbiota (GM) and blood metabolites on cholangiocarcinoma (CCA), we employed a Two-Step Mendelian Randomization (TSMR) approach. The core assumption of TSMR is the absence of interaction between the exposure (GM) and the mediator (blood metabolites). Within the TSMR framework, apart from obtaining the overall effect estimate of GM on CCA (b0), we computed two additional crucial estimates: 1. the causal effect of exposure (GM) on the mediator (plasma metabolites) (b1); 2. the causal effect of the mediator (blood metabolites) on CCA (b2). Based on these two effect estimates, we further calculated: the mediating effect, represented by the product of b1 and b2 (b1*b2); the indirect effect, derived by subtracting the mediating effect from the total effect (b0-b1*b2); and the ratio of the mediating effect to the total effect ([b1*b2]/b0).\u003c/b\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Statistical software\u003c/h2\u003e \u003cp\u003e \u003cb\u003eAll statistical analyses in this study were conducted using R software (version 4.2.1) with the following packages: \"TwoSampleMR\" (version 0.5.4), \"ieugwasr\" (version 0.1.5), \"ggplot2\" (version 3.4.3), \"MendelianRandomization\" (version 0.9.0), and \"TSMR\" (version 0.4.0).\u003c/b\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Ethics statement\u003c/h2\u003e \u003cp\u003e \u003cb\u003eThe GWAS summary statistics data used in our study were publicly available, and we obtained informed consent from all participating studies by following the protocols approved by their respective institutional review boards. No separate ethical approval was required for this study.\u003c/b\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\"\u003e\n \u003ch2\u003e3.1 Genetic causality and correlation between gut microbiota and CCA\u003c/h2\u003e\n \u003cp\u003e\u003cstrong\u003eWhen evaluating the causal relationship between gut microbiota (GM) and cholangiocarcinoma (CCA), we used five methods: MR Egger, weighted median, inverse variance weighted (IVW), simple mode, and weighted mode, primarily focusing on the IVW method. The results showed that two types of microbiota were positively correlated with CCA, while eight types were negatively correlated with CCA. Lachnospiraceae (OR 0.599, 95% CI 0.366 to 0.983, P\u0026thinsp;=\u0026thinsp;0.0425) and Eggerthella (OR 0.576, 95% CI 0.372 to 0.892, P\u0026thinsp;=\u0026thinsp;0.0134) exhibited protective effects against CCA. Enterobacteriaceae (OR 2.603, 95% CI 1.205 to 5.620, P\u0026thinsp;=\u0026thinsp;0.0149) and Enterobacteriales (OR 2.602, 95% CI 1.205 to 5.619, P\u0026thinsp;=\u0026thinsp;0.0149) significantly promoted the incidence of CCA. Additionally, Acidaminococcaceae (OR 1.583, 95% CI 1.074 to 2.333, P\u0026thinsp;=\u0026thinsp;0.0203), Gordonibacter (OR 1.369, 95% CI 1.076 to 1.743, P\u0026thinsp;=\u0026thinsp;0.0106), Bifidobacterium adolescentis (OR 1.413, 95% CI 1.024 to 1.948, P\u0026thinsp;=\u0026thinsp;0.0352), Bifidobacterium catenulatum (OR 1.420, 95% CI 1.045 to 1.928, P\u0026thinsp;=\u0026thinsp;0.0248), Gordonibacter pamelaeae (OR 1.374, 95% CI 1.044 to 1.807, P\u0026thinsp;=\u0026thinsp;0.0231), and Eubacterium eligens (OR 1.509, 95% CI 1.041 to 2.187, P\u0026thinsp;=\u0026thinsp;0.0300) showed potential causal links to increased CCA risk. The heterogeneity test results indicated that the effects of the gut microbiota instrumental variable were consistent across different subgroups (P-value\u0026thinsp;\u0026gt;\u0026thinsp;0.05), suggesting no significant effect heterogeneity in the study. The pleiotropy test results demonstrated that the gut microbiota instrumental variable did not significantly affect other potential confounding factors besides cholangiocarcinoma (P-value\u0026thinsp;\u0026gt;\u0026thinsp;0.05), indicating a high specificity of the selected instrumental variable (Supplementary Material 2). Further reverse MR analysis of GM and CCA to evaluate the relationship of the aforementioned ten microbiota with CCA revealed no significant positive results (Supplementary Material 3).\u003c/strong\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\"\u003e\n \u003ch2\u003e3.2 Genetic causality and correlation between plasma metabolites and CCA\u003c/h2\u003e\n \u003cp\u003e\u003cstrong\u003eWhen evaluating the causal relationship between plasma metabolites and cholangiocarcinoma (CCA), we used five methods: MR Egger, weighted median, inverse variance weighted (IVW), simple mode, and weighted mode, primarily focusing on the IVW method. The results showed that a total of 31 plasma metabolites had a causal relationship with CCA. Among them, Very Large Very Low-Density Lipoprotein Phospholipids (XXL_VLDL_PL; OR 1.340, 95% CI 1.043 to 1.723, P\u0026thinsp;=\u0026thinsp;0.022), Intermediate-Density Lipoprotein Triglycerides (IDL_TG; OR 1.252, 95% CI 1.031 to 1.520, P\u0026thinsp;=\u0026thinsp;0.023), Very Large Very Low-Density Lipoprotein Cholesterol (XXL_VLDL_C; OR 1.356, 95% CI 1.033 to 1.779, P\u0026thinsp;=\u0026thinsp;0.028), Extra Small Very Low-Density Lipoprotein Triglycerides (XS_VLDL_TG; OR 1.264, 95% CI 1.018 to 1.569, P\u0026thinsp;=\u0026thinsp;0.034), and Large High-Density Lipoprotein Phospholipids (L_HDL_PL; OR 1.287, 95% CI 1.011 to 1.638, P\u0026thinsp;=\u0026thinsp;0.040) were positively correlated with CCA, while the remaining 26 plasma metabolites were negatively correlated with CCA (see\u003c/strong\u003e Fig. \u003cspan\u003e3\u003c/span\u003e \u003cstrong\u003efor details). The heterogeneity test results indicated that the effects of the plasma metabolites instrumental variable were consistent across different subgroups (P-value\u0026thinsp;\u0026gt;\u0026thinsp;0.05), suggesting no significant effect heterogeneity in the study. The horizontal pleiotropy test results showed that the plasma metabolites instrumental variable did not significantly affect other potential confounding factors besides cholangiocarcinoma (P-value\u0026thinsp;\u0026gt;\u0026thinsp;0.05), indicating high specificity of the selected instrumental variable (Supplementary Material 4). Similarly, we conducted a reverse MR analysis of plasma metabolites and CCA, but no significant positive results were observed (Supplementary Material 5).\u003c/strong\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\"\u003e\n \u003ch2\u003e3.3 Mediation analyses of potential blood metabolites\u003c/h2\u003e\n \u003cp\u003e\u003cstrong\u003eWe conducted a bidirectional Mendelian randomization (MR) analysis between 10 types of gut microbiota identified in the first screening step and 31 plasma metabolites identified in the second screening step. The study results (Supplementary Material 3) showed that 4 types of gut microbiota could cause changes in CCA by acting on 10 plasma metabolites (Supplementary Material 6). This indicates that plasma metabolites can serve as mediators in the causal relationship between gut microbiota and CCA. In\u003c/strong\u003e Fig. \u003cspan\u003e4\u003c/span\u003e, \u003cstrong\u003eIntermediate-Density Lipoprotein Triglycerides (IDL_TG; OR 1.252, 95% CI 1.031 to 1.520, P\u0026thinsp;=\u0026thinsp;0.023) is positively correlated with CCA, while the remaining plasma metabolites are negatively correlated with the risk of developing CCA. We calculated the indirect effects and proportions mediated by these metabolites, finding that the effects of L_LDL_FC and IDL_TG remained significant for f_Enterobacteriaceae and o_Enterobacteriales after adjusting for gut microbiota. Similarly, in s_Bifidobacterium_adolescentis, the effect of XS_VLDL_CE persisted; and in s_Eubacterium_eligens, XXL_VLDL_C also played a role. Overall, we observed indirect effects of L_LDL_FC and IDL_TG in the association between f_Enterobacteriaceae and o_Enterobacteriales and CCA, with mediation proportions of 2.2% (P\u0026thinsp;=\u0026thinsp;0.038) and 1.4% (P\u0026thinsp;=\u0026thinsp;0.032), respectively; and mediation proportions of XS_VLDL_CE and XXL_VLDL_C between s_Bifidobacterium_adolescentis, s_Eubacterium_eligens, and CCA were 3.5% (P\u0026thinsp;=\u0026thinsp;0.006) and 3.0% (P\u0026thinsp;=\u0026thinsp;0.025), respectively.\u003c/strong\u003e\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003e \u003cb\u003eCholangiocarcinoma (CCA) is a malignant tumor originating from the epithelial cells of the bile ducts, with an increasing incidence worldwide\u003c/b\u003e \u003csup\u003e[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e. \u003cb\u003eAlthough CCA accounts for only 15% of primary liver malignancies, most cases are diagnosed at advanced stages with poor prognosis. Only 30% of tumors are curable at the time of diagnosis, and the five-year survival rate ranges from 7\u0026ndash;20%, posing a significant threat to public health\u003c/b\u003e\u003csup\u003e[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/sup\u003e. \u003cb\u003eThe human gut hosts a complex microbial community, including bacteria, fungi, archaea, viruses, and protozoa, which play a crucial role in maintaining human health\u003c/b\u003e\u003csup\u003e[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/sup\u003e. \u003cb\u003eThe gut microbiota coexists with the intestinal mucosa, providing essential immune, metabolic, and gastrointestinal protective functions for healthy individuals\u003c/b\u003e\u003csup\u003e[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/sup\u003e. \u003cb\u003eA reduction in microbial diversity may increase susceptibility to various diseases, including cancer\u003c/b\u003e\u003csup\u003e[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003cb\u003eIn recent years, extensive research has been conducted on the role of gut microbiota in the occurrence and development of CCA and its impact on diagnostic and therapeutic strategies\u003c/b\u003e \u003csup\u003e[\u003cspan additionalcitationids=\"CR40\" citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]\u003c/sup\u003e. \u003cb\u003eFor example, studies have shown that an increase in genetically predicted Veillonellaceae, Alistipes, Enterobacteriaceae, and Firmicutes is associated with a higher risk of intrahepatic cholangiocarcinoma (ICC), while an increase in anaerobic bacteria, Paraprevotella, Parasutterella, and Verrucomicrobia appears to be protective. Bioinformatics analyses have indicated that differentially expressed genes near gut microbiome-associated loci may influence ICC through regulatory pathways and the tumor immune microenvironment\u003c/b\u003e\u003csup\u003e[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]\u003c/sup\u003e. \u003cb\u003eThese findings suggest an important role of the gut microbiome in CCA occurrence and development, but comprehensive causal relationship analyses between gut microbiota and CCA are still lacking in the literature.\u003c/b\u003e\u003c/p\u003e \u003cp\u003e \u003cb\u003ePlasma metabolites play a crucial role in energy metabolism, signal transduction, and immune regulation in the body. Metabolic disturbances are closely related to the occurrence and development of various diseases, including cancer\u003c/b\u003e \u003csup\u003e[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]\u003c/sup\u003e. \u003cb\u003ePlasma metabolites are small molecules widely present in the blood, including amino acids, lipids, sugars, and organic acids. These metabolites maintain normal physiological functions and metabolic balance by participating in various biochemical reactions and metabolic pathways. For example, studies have shown significant associations between different plasma metabolites and cancer risk. Circulating metabolites such as O-methyl catechol sulfate and 4-ethylphenyl sulfate are significantly associated with the risk of lung cancer and renal cell carcinoma, respectively\u003c/b\u003e \u003csup\u003e[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]\u003c/sup\u003e. \u003cb\u003eThese studies suggest that plasma metabolites may play an important role in cancer occurrence and development. Therefore, our study is the first to use summary statistics from genome-wide association studies (GWAS) to conduct two-sample MR and TSMR analyses to explore the potential causal links between gut microbiota, plasma metabolites, and CCA. Through this analytical approach, we aim to provide new insights for effective prevention and intervention strategies for CCA and to gain a deeper understanding of the pathogenesis of CCA from the perspective of the gut microbiome.\u003c/b\u003e\u003c/p\u003e \u003cp\u003e \u003cb\u003eIn the causal relationship analysis between the gut microbiome and CCA, we found that two microbes were positively correlated with CCA, while eight microbes were negatively correlated. Specifically, Enterobacteriaceae and Enterobacteriales significantly increased the risk of CCA, which aligns with previous research linking gut pathogens to tumor development. For instance, some pathogenic bacteria in the gut microbiome can induce chronic inflammation and cellular proliferation of the intestinal mucosa by producing toxins and metabolic products, thereby increasing the risk of tumor development. Conversely, Lachnospiraceae and Eggerthella showed protective effects against CCA, suggesting that these microbiota might reduce cancer risk by inhibiting inflammatory responses or modulating immune function. These findings not only deepen our understanding of the role of the gut microbiome in cancer development but also provide new ideas for future prevention or treatment of CCA by regulating the gut microbiota.\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eWe further analyzed the causal relationship between plasma metabolites and CCA and found that 31 metabolites were significantly associated with CCA. Among them, very low-density lipoprotein phospholipids (XXL_VLDL_PL), intermediate-density lipoprotein triglycerides (IDL_TG), very low-density lipoprotein cholesterol (XXL_VLDL_C), very small very low-density lipoprotein triglycerides (XS_VLDL_TG), and large high-density lipoprotein phospholipids (L_HDL_PL) were positively correlated with CCA. These results indicate that lipid metabolism disorders may be an important risk factor for the development of CCA. In particular, lipoprotein-associated metabolites may play a key role in the pathophysiology of cancer. Lipoprotein metabolism disorders in the blood may lead to hepatic steatosis and inflammatory responses, thereby promoting the development of liver cancer. Therefore, interventions targeting these metabolites may provide new pathways for the prevention and treatment of CCA.\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eThrough Two-Step MR (TSMR) analysis, we found that plasma metabolites mediate the relationship between the gut microbiome and CCA. Specifically, Intermediate-Density Lipoprotein Triglycerides (IDL_TG) significantly mediated the association between f_Enterobacteriaceae and o_Enterobacteriales and CCA. This suggests that these plasma metabolites may influence the risk of CCA by affecting the metabolic function of gut microbiota. Additionally, XS_VLDL_CE and XXL_VLDL_C significantly mediated the association between s_Bifidobacterium_adolescentis and s_Eubacterium_eligens and CCA, respectively. These findings further support the hypothesis that plasma metabolites act as mediators of the gut microbiota's impact on cancer risk and provide clear directions for future research, such as exploring how to intervene in the gut microbiota by regulating plasma metabolites to reduce the risk of CCA.\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eThis study is the first to systematically reveal the causal relationships and complex interactions between the gut microbiome and plasma metabolites and CCA. This not only enriches our understanding of the pathogenesis of CCA but also provides new potential targets for future prevention and treatment strategies. Specifically, regulating the metabolic pathways of the gut microbiota and plasma metabolites may achieve early intervention and precision treatment of CCA. However, the study has certain limitations, such as limited sample size and variability among different populations. Future research should further validate these findings and explore their clinical application value.\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eOverall, this study provides new insights into the role of the gut microbiome and plasma metabolites in CCA occurrence, suggesting that comprehensive regulation of these biomarkers may achieve effective prevention and control of CCA. Future research should continue to explore these mechanisms and develop targeted interventions to improve the prognosis of CCA patients.\u003c/b\u003e \u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003e \u003cb\u003eTo our knowledge, this is the first comprehensive study to evaluate the causal relationships between gut microbiota, plasma metabolites, and cholangiocarcinoma (CCA). These findings underscore the importance of uncovering the potential mechanisms linking gut microbiota and CCA. The results of this study provide new insights into the application of microbiome-based therapies and metabolite-targeted interventions in CCA. By further exploring these mechanisms, it is hoped that more effective prevention and treatment strategies can be developed to improve the prognosis of CCA patients.\u003c/b\u003e \u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the National Natural Science Foundation of China (grant number 82003073, www.nsfc.gov.cn), the Guangdong Science and Technology Department (grant number 2020A1515011296, http://pro.gdstc.gov.cn) and the National Natural Science Foundation of China (No. 81971463).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of interest\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no financial conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe original contributions presented in the study are included in the article/Supplementary material, further inquiries can be directed to the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contribution\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConceptualization: SX, XH ,LL. Study design: SX, HY, JR, QZ, WL. Methodology: SX, LL, JR. Data collection: all authors. Investigation: all authors. Statistical analysis: SX, XH, QZ. Writing\u0026mdash;original draft: SX, XH, LL. Writing\u0026mdash;review \u0026amp; editing: all authors. Funding acquisition: HY, WL. Approval of final manuscript: all authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics statement\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe GWAS summary statistics data used in our study were publicly available, which obtained informed consent from all participating studies by following the protocols approved by their respective institutional review boards. No separate ethical approval was required for this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSarcognato S, Sacchi D, Fassan M, et al. Cholangiocarcinoma[J]. 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Gut microbiota regulates the ALK5/NOX1 axis by altering glutamine metabolism to inhibit ferroptosis of intrahepatic cholangiocarcinoma cells[J]. Biochimica Et Biophysica Acta. Molecular Basis of Disease, 2024, 1870(5): 167152.\u003c/li\u003e\n\u003cli\u003eWheatley R C, Kilgour E, Jacobs T, et al. Potential influence of the microbiome environment in patients with biliary tract cancer and implications for therapy[J]. British Journal of Cancer, 2022, 126(5): 693-705.\u003c/li\u003e\n\u003cli\u003eChen Z, Shi W, Chen K, et al. Elucidating the causal association between gut microbiota and intrahepatic cholangiocarcinoma through Mendelian randomization analysis[J]. Frontiers in Microbiology, 2023, 14: 1288525.\u003c/li\u003e\n\u003cli\u003eWang J, Zhou X, Li X, et al. Fecal Microbiota Transplantation Alters the Outcome of Hepatitis B Virus Infection in Mice[J]. Frontiers in Cellular and Infection Microbiology, 2022, 12: 844132.\u003c/li\u003e\n\u003cli\u003eVidman L, Zheng R, Bod\u0026eacute;n S, et al. Untargeted plasma metabolomics and risk of colorectal cancer-an analysis nested within a large-scale prospective cohort[J]. Cancer \u0026amp; Metabolism, 2023, 11(1): 17.\u003c/li\u003e\n\u003cli\u003eChen Y, Xie Y, Ci H, et al. Plasma metabolites and risk of seven cancers: a two-sample Mendelian randomization study among European descendants[J]. BMC medicine, 2024, 22(1): 90.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table 2","content":"\u003cp\u003eTable 2 is available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Cholangiocarcinoma, gut microbiota, metabolites, mediator factors, causal inference, Mendelian randomization","lastPublishedDoi":"10.21203/rs.3.rs-4687408/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4687408/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground:\u003c/h2\u003e \u003cp\u003eCholangiocarcinoma (CCA) is an aggressive tumor with a poor prognosis. Numerous animal experiments and clinical studies have indicated an association between the gut microbiota (GM) and the incidence of CCA. Additionally, patients with CCA often exhibit metabolic disorders, but there is a lack of evidence regarding causality. Therefore, elucidating the causal relationships among GM, plasma metabolites, and CCA is crucial and can provide insights for the prevention and treatment of CCA.\u003c/p\u003e\u003ch2\u003eObjective:\u003c/h2\u003e \u003cp\u003eWe utilized summary statistics from the largest available genome-wide association studies, including gut microbiota (GM) data from the MiBioGen consortium (n\u0026thinsp;=\u0026thinsp;18,340), plasma metabolites from four distinct human metabolomics studies, and cholangiocarcinoma (CCA) patient data from the UK Biobank (cases n\u0026thinsp;=\u0026thinsp;832 and controls n\u0026thinsp;=\u0026thinsp;475,259). We conducted bidirectional Mendelian randomization analyses to explore the causal relationship between GM and CCA. Additionally, we performed two mediation analyses and a two-step Mendelian randomization (MR) to identify potential mediating metabolites, offering guidance for the clinical early detection and intervention of CCA.\u003c/p\u003e\u003ch2\u003eResults:\u003c/h2\u003e \u003cp\u003eIn our analysis, we identified that two types of gut microbes (Enterobacteriaceae and Enterobacteriales) increase the risk of cholangiocarcinoma (CCA), while eight types of gut microbes, including Lachnospiraceae and Eggerthella, have protective effects. Additionally, we identified 31 plasma metabolites significantly associated with CCA, with lipid metabolism disorders being a key factor. Notably, four plasma metabolites, such as Intermediate-Density Lipoprotein Triglycerides (IDL_TG), mediate the relationship between gut microbiota and CCA, highlighting the role of plasma metabolites as intermediaries. These findings underscore the potential of targeting gut microbiota and plasma metabolites for the prevention and treatment of CCA.\u003c/p\u003e\u003ch2\u003eConclusion:\u003c/h2\u003e \u003cp\u003eOur research demonstrates that plasma metabolites play a pivotal role in the pathogenesis of CCA induced by gut microbiota. This finding deepens our understanding of how gut microbiota dysbiosis contributes to the development of CCA by influencing plasma metabolites.\u003c/p\u003e","manuscriptTitle":"Causal Relationship Among Intestinal Microbiota, Lipid Metabolites, and Cholangiocarcinoma: A Mendelian Randomization Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-10 11:54:47","doi":"10.21203/rs.3.rs-4687408/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ba712f5b-3a80-4d26-89ee-132c8cc8e35e","owner":[],"postedDate":"August 10th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-10-28T15:23:05+00:00","versionOfRecord":[],"versionCreatedAt":"2024-08-10 11:54:47","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4687408","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4687408","identity":"rs-4687408","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}