{"paper_id":"48c4c8cc-4d5e-4086-9cdf-b2d9fccf1d7b","body_text":"Gut microbiota and radiculopathy: a bidirectional two-sample 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 Gut microbiota and radiculopathy: a bidirectional two-sample Mendelian randomization study Jinyv Wang, Chen Yan, Linhui Han, YiJuan Lu, JingChuan Sun, Jiangang Shi, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3863003/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 Previous studies have suggested a potential link between the gut microbiome and radiculopathy, but the causal relationship remains unclear. Therefore, the aim of this study was to determine the causal effect of gut microbiome on radiculopathy using Mendelian randomization (MR) approach and single nucleotide polymorphisms (SNPs) associated with gut microbiome as instrumental variables Methods Summary data from genome-wide association studies of gut microbiota (the MiBioGen) and radiculopathy (the FinnGen biobank) were acquired. The inverse variance weighting (IVW) was chosen as the main MR Analysis method. The weighted median, MR-Egger regression, weighted model, and simple model were provided as additional supplements. Mendelian randomization pleiotropy residual sum and outlier (MR-PRESSO) and MR-Egger regression were performed to evaluate the horizontal pleiotropy and to eliminate outlier single-nucleotide polymorphisms (SNPs). Cochran’s Q-test was applied for heterogeneity detection. Results We identified eight gut microbial taxa that were causally associated with radiculopathy (P<0.05). The Cochrane Q test produced results that did not indicate heterogeneity (P>0.05), indicating a lack of statistical significance. Furthermore, both the MR-Egger intercept test and the MR-PRESSO global test indicated that our findings were not influenced by horizontal pleiotropy (P>0.05), further supporting the reliability of our results. In the reverse analysis, no evidence was found to suggest that radiculopathy has an impact on the gut microbiota. Conclusion We identified four gut microbiota that were protective against radiculopathy and four that may elevate the risk of the condition. Our findings confirmed a potential causal link between gut microbiota and radiculopathy, thereby providing a theoretical foundation for the development of targeted prevention strategies. Keywords Gut microbiome Mendelian randomization radiculopathy Intervertebral disc degeneration pain Figures Figure 1 Figure 2 Figure 3 Introduction Radiculopathy is one of the major spine degeneration-related diseases that affects or damages the nerve roots, leading to abnormal sensation and movement of the affected nerve roots. This dysfunction is often caused by a herniated disc, which compresses the nerve roots and prevents them from functioning normally. The symptoms of radiculopathy are diverse, yet the most common presentations include pain and focal neurological disturbances. Furthermore, individuals may experience paraesthesia, numbness, muscle weakness, atrophy, and other related symptoms[1]. The human gut is the home to trillions of bacterial cells, which play a pivotal role in regulating host metabolites, vitamin synthesis, and immune homeostasis. These bacteria have also been linked to the development of various diseases, highlighting their significance in maintaining overall health. Recent studies have reported a causal relationship between gut microbes and disc degeneration as well as other spinal degenerative diseases[2-4]. Although it has been reported in the literature that it is associated with radiculopathy, the causal relationship has not been clarified. Mendelian randomization is a new statistical technique that employs genetic variation as a tool for detecting and quantifying causal relationships[5].This approach offers the advantage of overcoming the potential impact of confounding and reverse causality by utilizing genetic markers that are randomly allocated at conception and remain stable throughout life. Therefoe, In this study,we conducted a causal analysis of radiculopathy by utilizing aggregated data of gut microbiome.Our objective was to identify intestinal microbiome taxa that may significantly impact the development of radiculopathy,thereby providing a foundation for subsequent treatment and diagnosis. 2. Materials and methods 2.1Ethics approval statement Each GWAS involved in this study was ethically approved by the respective institutions. 2.2Study design We used two-sample Mendelian randomization to evaluate the causal effect of gut microbiota on radiculopathy with single nucleotide polymorphisms (SNPS) associated with various gut microbes as IVs. Our findings were presented in accordance with the STROBE-MR guidelines. MR studies require the fulfillment of three assumptions: (1) a robust correlation between the instrumental variable (IV) and the exposure, (2) the IVs are independent of confounding factors, and (3) the IVs solely influence the outcome through their association with the exposure. 2.3Exposure data of gut microbiota Summary data on gut microbes were obtained from a large-scale GWAS study conducted by MiBioGen. This study collected 24 cohorts consisting of 18,340 participants, mainly from European and American countries. The host genotype and 16S fecal microbiome rRNA gene sequencing profiles of participants were studied, and a total of 211 microbial taxa were analyzed by quantitative trait loci.[6] 2.4Outcome data of radiculopathy The data for radiculopathy is derived from the FinnGen database of Genetic Statistics published in May 2023, version DF9 .FinnGen is a large public-private partnership aiming to collect and analyse genome and health data from 500,000 Finnish biobank participants. In the DF9 version, 377,277 samples (210 870 women and 166 407 men) were collected, which yielded 20,175,454 single-nucleotide polymorphisms (SNPS) for analysis after adjustment for age, sex, and genotyping batch.[7] 2.5Genetic instruments selection and harmonization Mendelian randomization (MR) employs genetic variation as an instrumental variable (IV) to infer causal relationships between exposure and outcome. To ensure confidence in the findings, the selection of IVs must adhere to three principles: (1) IVs must be independent of confounding factors; (2) IVs must be strongly correlated with exposure factors; and (3) IVs must not be associated with outcome variables except through their correlation with the exposure. As the number of IVs obtained under the strict threshold (P < 5 × 10−8) is limited, we employed a more comprehensive threshold (P < 1 × 10−5) to acquire a larger number of IVs. Additionally, to ensure the independence of each IV, SNPs with a threshold of r2 < 0.001 within a 500 kb window size were pruned to mitigate linkage disequilibrium (LD). The strength of each SNP was represented by the F statistic, which was calculated using the formula: F = R2 × (N − 1 − K)/ [(1 – R2)× K] In this formula, R2 is the proportion of variability explained by each SNP, N is the GWAS sample size, and k is the number of SNPS. A statistic of F greater than 10 indicates that there is no convincing evidence of instrumental bias. 2.6Statistical analysis In order to explore the causal relationship between intestinal microorganisms and radiculopathy, inverse variance weighting (IVW) method was used as the main analysis method to determine the causal relationship (P<0.05), and MR-Egger, weighted median, simple model and weighted model were used as supplementary analysis methods. The Cochran Q test was utilized to assess the heterogeneity among SNPs. A P-value exceeding 0.05 denoted a low likelihood of heterogeneity among SNPs. To evaluate potential pleotropic effects, MR-Egger regression analysis was conducted to ensure the independence of instrumental variables from confounders. Additionally, MR Pleotropic residuals and outliers (MR-PRESSO) tests were performed. Odds ratios (ORs) and 95% confidence intervals (CIs) were utilized to represent the relationship between gut microbiota and radiculopathy(Fig1). All statistical analyses were conducted using R version 4.2.3. The “Two-sample MR” and “MRPRESSO” R packages were used for statistical analysis in R version 4.2.3. The statistical threshold for causal effect evidence was set at P < 0.05. 3. Results 3.1. The selection of IVs related to gut microbiome After taking into account the linkage disequilibrium effect, palindromic effect and weak tool bias, 2350 SNPS from 191 bacterial species were taken as IVs. These taxa include 9 phyla (106 SNPs), 16 classes (189 SNPs), 20 orders (231 SNPs), 35 families (413 SNPs), and 131 genera (1411 SNPs). For further analysis, we collected key SNP data including effector alleles, beta, standard error (SE), and P-values. The F-statistics of IVs were all generally greater than 10, indicating no evidence of weak instrument bias. 3.2 Causal effects of gut microbiota on radiculopathy A total of 8 causal associations from gut microbiota features (1 class and 7 genera) to radiculopathy traits were identified by the IVW method. As shown in Figure 3, IVW analysis showed a causal relationship between Class Betaproteobacteria (OR: 0.76,95%CI: 0.59-0.99,P=0.042), genus Allisonella(OR: 1.15,95%CI: 1.03-1.29,P=0.010), genus Terrisporobacter(OR: 1.31,95%CI: 1.05-1.62,P=0.014), genus Veillonella(OR: 0.73,95%CI: 0,57-0.94,P=0.015), genus LachnospiraceaeNC2004group(OR: 0.76,95%CI: 0,64-0.90,P=0.002), genus Marvinbryantia(OR: 1.28,95%CI: 1.04-1.56,P=0.015), genus Olsenella(OR: 0.87,95%CI: 0.76-0.89,P=0.027), genus Oxalobacter(OR: 1.18,95%CI: 1.05-1.32,P=0.003)and radiculopathy(Fig2,3,Table1).According to the Cochrane Q and MR-Egger tests, no heterogeneity was observed, and the analysis results of MR-Egger and MRPRESSO global tests indicated the absence of horizontal pleiotropy(Supplementary material 1). In reverse MR Analysis, we found that radiculopathy had no significant causal effect on these eight gut microbiota(Supplementary material 2). Table 1MR estimates for the association between gut microbiota and radiculopathy. MR method, method used for Mendelian randomization analysis; nSNP, the number of SNPs selected for MR analysis; OR, odds ratio; OR-low, the lower limit of the confidence interval of OR; OR-high, the upper limit of the confidence interval of OR. Exposure MR method nSNP OR OR-low OR-high P-value class.Betaproteobacteria MR Egger 10 1.3716452 0.654658852 2.873879348 0.43 class.Betaproteobacteria Weighted median 10 0.662552948 0.472400455 0.929246372 1.70E-02 class.Betaproteobacteria Inverse variance weighted 10 0.769773901 0.597486854 0.991740411 4.30E-02 class.Betaproteobacteria Simple mode 10 0.57721211 0.318941787 1.044622666 0.1 class.Betaproteobacteria Weighted mode 10 0.594262983 0.334050731 1.057170246 0.11 genus.Allisonella MR Egger 8 1.753657305 0.82553478 3.725238496 0.19 genus.Allisonella Weighted median 8 1.158928674 1.000538528 1.342392756 4.90E-02 genus.Allisonella Inverse variance weighted 8 1.156965338 1.035116314 1.293157857 1.00E-02 genus.Allisonella Simple mode 8 1.174911138 0.942218544 1.465070064 0.2 genus.Allisonella Weighted mode 8 1.163820865 0.944194345 1.434534123 0.2 genus.Terrisporobacter MR Egger 5 1.862941078 0.964475477 3.598380203 0.16 genus.Terrisporobacter Weighted median 5 1.370171923 1.027816565 1.826562409 3.20E-02 genus.Terrisporobacter Inverse variance weighted 5 1.310734015 1.054442104 1.629320046 1.50E-02 genus.Terrisporobacter Simple mode 5 1.415014185 0.954287425 2.098178276 0.16 genus.Terrisporobacter Weighted mode 5 1.377822422 0.922765796 2.057287596 0.19 genus.Veillonella MR Egger 5 1.161942416 0.003856414 350.0947096 0.96 genus.Veillonella Weighted median 5 0.765118042 0.560789815 1.043894882 0.09 genus.Veillonella Inverse variance weighted 5 0.736895008 0.575893069 0.942908124 1.50E-02 genus.Veillonella Simple mode 5 0.820293617 0.531614769 1.265731611 0.42 genus.Veillonella Weighted mode 5 0.838305598 0.550829514 1.27581449 0.46 genus.LachnospiraceaeNC2004group MR Egger 9 0.574322148 0.28600439 1.153289744 0.16 genus.LachnospiraceaeNC2004group Weighted median 9 0.825097671 0.660980939 1.029963388 0.09 genus.LachnospiraceaeNC2004group Inverse variance weighted 9 0.767976785 0.648864568 0.908954459 2.10E-03 genus.LachnospiraceaeNC2004group Simple mode 9 0.846177362 0.575894917 1.24331038 0.42 genus.LachnospiraceaeNC2004group Weighted mode 9 0.851605658 0.601203295 1.2063011 0.39 genus.Marvinbryantia MR Egger 11 1.220795266 0.555326936 2.683718333 0.63 genus.Marvinbryantia Weighted median 11 1.090269256 0.844693683 1.407240369 0.51 genus.Marvinbryantia Inverse variance weighted 11 1.28059704 1.04856999 1.563966922 1.50E-02 genus.Marvinbryantia Simple mode 11 1.078480655 0.712485157 1.632483866 0.73 genus.Marvinbryantia Weighted mode 11 1.053208689 0.759937479 1.459657632 0.76 genus.Olsenella MR Egger 10 1.026965168 0.686918438 1.535345971 0.9 genus.Olsenella Weighted median 10 0.875200641 0.747004671 1.02539675 0.1 genus.Olsenella Inverse variance weighted 10 0.870179464 0.768930403 0.984760515 2.80E-02 genus.Olsenella Simple mode 10 0.823303749 0.641753618 1.056213856 0.16 genus.Olsenella Weighted mode 10 0.892767231 0.716400419 1.112552851 0.34 genus.Oxalobacter MR Egger 11 1.366522791 0.786470664 2.374385497 0.3 genus.Oxalobacter Weighted median 11 1.167015992 1.000132881 1.361745375 5.00E-02 genus.Oxalobacter Inverse variance weighted 11 1.184313631 1.057371854 1.326495282 3.50E-03 genus.Oxalobacter Simple mode 11 1.138222141 0.8940882 1.449017716 0.32 genus.Oxalobacter Weighted mode 11 1.144322948 0.887758336 1.475035441 0.32 4. Discussion In this present study, we utilized GM data from a GWAS meta-analysis conducted by the MiBioGen Consortium and radiculopathy data published by the FinnGen Alliance R9. We aimed to revealthe causal relationship between gut microbiota and radiculopathy. The results identified eight gut microbiota that exhibited a causal relationship with radiculopathy. Furthermore, we conducted a reverse analysis to establish a unidirectional causal relationship, revealing no evidence that radiculopathy has an impact on the gut microbiota. At present, the relationship between gut microbiota and orthopedic diseases is increasingly recognized. The impact of oral, gut, and skin microbiota on the causal relationship of the skeletal nervous system has been widely reported, leading to diseases such as disc herniation, lower back pain, and spinal deformities[8-9]. Short-Chain Fatty Acids (SCFAs) are a class of organic acids produced by the fermentation of dietary fiber in the gut by microbiota, with major SCFAs including propionic acid, acetic acid, and butyric acid. SCFAs are believed to reduce the occurrence of osteoporosis by influencing the proliferation and differentiation of osteoblasts and osteoclasts. In a mouse model of ovariectomy, SCFAs effectively reduce bone loss[10]. Disc degeneration is a significant factor leading to radiculopathy, and research has found 58 overlapping bacteria between intervertebral discs (IVD) and the gut, suggesting a connection between IVD and the gut[11]. Rajasekaran and others evaluated 24 lumbar IVDs and reported differences in the microbial composition between healthy discs and degenerative discs, indicating that gut microbiota may translocate through the intestinal epithelial barrier into intervertebral discs[12]. Wentian Li and colleagues found a blood-brain barrier-like function in intervertebral discs, hindering the immune system's surveillance of the disc's interior[10]. The lack of immune surveillance and hypoxic conditions in intervertebral discs provide ideal conditions for anaerobic bacteria to proliferate during invasion. Furthermore, damage to the intestinal epithelium can lead to bacteria and their toxic metabolites entering circulation, potentially triggering remote inflammation of intervertebral discs, resulting in disc degeneration. Compared to the typical gut microbiota, the composition of the gut microbiota in patients with chronic inflammatory pain shows significant changes in Actinobacteria, Firmicutes, and Bacteroidetes[13]. Considering the alteration of the microbiota as a potential therapeutic approach to maintaining or promoting skeletal nervous health is a rapidly developing research field. Our study elucidates the causal relationship between gut microbiota and radiculopathy, filling a gap in this field and laying the foundation for further understanding the mechanisms. Radiculopathy are mainly caused by chronic compression or infringement of nerve roots, with major contributing factors including ectopic ossification leading to bone spur formation, spinal canal stenosis, disc protrusion compression, osteoporosis, and local inflammation. Magnetic Resonance (MR) analysis of the gut microbiota and intervertebral disc degeneration shows that the presence of the Marvinbryantia microbial group is an important risk factor for intervertebral disc degeneration, consistent with our research results[3]. Currently, the influence of gut microbiota on osteoporosis and intervertebral disc lesions has been elucidated, which may be an indirect influencing factor for the occurrence of radiculopathy[14-15]. Experimental evidence suggests that SCFAs can inhibit the differentiation of primary bone marrow (BM) cells into osteoclasts, and butyrate salts can induce osteoblast differentiation and the formation of mineral nodules[16]. However, further basic experiments are needed to verify whether they can lead to heterotopic ossification. Gut microbiota plays a crucial role in the synthesis and absorption of vitamins, as they can synthesize B-group vitamins, vitamin K, and regulate the absorption of vitamins A, D, E, and K. Many studies have established a connection between vitamin K and B deficiencies and intervertebral disc degeneration. Additionally, deficiencies in vitamins B and D may affect nerve function[17-18]. It is speculated that gut microbiota may indirectly affect the development of radiculopathy through this pathway. Gut microbiota may influence radiculopathy through inflammation. Dietary tryptophan, a metabolite of Clostridium, has been found to directly act on astrocytes, limiting inflammation to alleviate neuropathic pain in mice[19]. Colonization of Bifidobacterium causes relapses in sterile mice with arthritis. In the experimental autoimmune encephalomyelitis (EAE) model, polysaccharide A (PSA) produced by Bacteroides fragilis promotes the development of regulatory T cells (Tregs) and provides protection against central nervous system inflammation and demyelination. Moreover, the gut microbiota can regulate chronic inflammatory pain through metabolites. Anaerobic bacteria such as Bacteroides, Bifidobacterium, and other bacteria produce short-chain fatty acids, which regulate T cell activation by binding to specific G protein-coupled receptors[20]. This process increases histone acetylation, ultimately regulating chronic inflammatory pain. Certain cell wall components, such as lipopolysaccharides (LPS), lipoteichoic acid (LTA), peptidoglycan (PGN), and β-glucan, can enter the circulation and promote the development of chronic inflammatory pain[22,26]. Clarifying the molecular mechanisms by which the gut microbiota regulates pain may be a new drug target for the future treatment of peripheral nerve pain, including radiculopathy. Non-steroidal anti-inflammatory drugs and glucocorticoids are commonly used for the conservative treatment of radiculopathy[25]. Studies have found that intraperitoneal injection of dexamethasone in rats can regulate the gut microbiota, affecting the abundance of aerobic bacteria and lactobacilli, depending on the concentration. In a study of 155 people, ibuprofen was found to increase the abundance of Enterococcaceae, Enterobacteriaceae, Propionibacteriaceae, Pseudomonadaceae, Pseudonocardiaceae, and Salmonellaceae[23]. These studies suggest that painkillers commonly used for radiculopathy may affect the gut microbiota, and whether these effects in turn influence radiculopathy still requires further investigation. Solving orthopedic diseases from the perspective of gut microbiota is gaining increasing attention. In a clinical experiment in Germany, it was shown that a high-fiber diet effectively reduces bone absorption. Fecal transplantation and the use of probiotics protect bone health by inhibiting inflammatory reactions, regulating host immune responses, and maintaining gut microbiota, indirectly reducing the incidence of radiculopathy[21,24]. However, addressing nerve pain from the perspective of the gut microbiota is still in its early stages, and more experiments are needed to verify the relationship between the gut microbiota and radiculopathy. This study represents the pioneering utilization of gut microbiome in Mendelian randomization analysis for investigating radiculopathy. A fundamental assumption of Mendelian randomization, which leverages an individual's genotype information to estimate causal effects, is that the genetic variation employed must exhibit a robust association with the exposure of interest while being independent from confounding factors. By satisfying these assumptions, Mendelian randomization can mitigate the impact of various uncontrolled confounders inherent in traditional observational studies and enhance the precision of inferring causality between gut microbiome and radiculopathy. Mendelian randomization has been extensively applied to elucidate the causal relationship between gut microbiota and skeletal nerve diseases such as disc degeneration, rheumatoid arthritis, and low back pain. Nevertheless, our study does have certain limitations. Firstly, due to concerns regarding population stratification bias, most participants included in our aggregated GWAS data were of European ancestry; however, radiculopathy predominantly occurs in East Asia. Consequently, it remains unclear whether intestinal microbes exert a causal influence on radiculopathy within Asian populations, potentially introducing some degree of error into our findings. Secondly, in order to incorporate more SNPs as instrumental variables (IVs), some SNPs utilized in our analysis did not meet the standard significance threshold (5*10-8), which may compromise result reproducibility 5. Conclusion Using publicly available GWAS data, we performed a bidirectional, two-sample Mendelian randomization analysis of the causal relationship between gut microbiota and radiculopathy in 211 participants. The results of our analysis identified eight gut microbiota causally associated with radiculopathy. For more accurate results, more advanced MR Analysis and more aggregated GWAS data for gut microbiota and radiculopathy patients are needed. Declarations Funding : This study was supported by the National Natural Science Foundation of China (SHDC2020CR1024B) Conflicts of interest/Competing interests : None. Author Contribution Jinyv Wang wrote the main manuscript text .Chen Yan and Linhui Han are responsible for data collection and analysisYiJuan Lu prepared figures 1-3. All authors reviewed the manuscript. References Nikolaidis I, Fouyas IP, Sandercock PA, Statham PF. Surgery for cervical radiculopathy or myelopathy. Cochrane Database Syst Rev. 2010;2010(1):CD001466. doi: 10.1002/14651858.CD001466.pub3 . PMID: 20091520; PMCID:PMC7084060. Su M, Tang Y, Kong W, Zhang S, Zhu T. Genetically supported causality between gut microbiota, gut metabolites and low back pain: a two-sample Mendelian randomization study. Front Microbiol. 2023;14:1157451. doi: 10.3389/fmicb.2023.1157451 . 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Advances in pathogenesis and therapeuticstrategies for osteoporosis. Pharmacol Ther. 2022;237:108168. doi: 10.1016/j.pharmthera.2022.108168 . Epub 2022 Mar 10. PMID: 35283172. Wu T, et al. Chronic glucocorticoid treatment induced circadian clock disorder leads to lipid metabolism and gut microbiota alterations in rats. Life Sci. 2018;192:173–182. doi: 10.1016/j.lfs.2017.11.049 . Epub 2017 Dec 1. PMID: 29196049. Li JS, et al. Potential roles of gut microbiota and microbial metabolites in chronic inflammatory pain and the mechanisms of therapy drugs. Ther Adv Chronic Dis. 2022;13:20406223221091177. doi: 10.1177/20406223221091177 . PMID: 35924009; PMCID:PMC9340317. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {\"props\":{\"pageProps\":{\"initialData\":{\"identity\":\"rs-3863003\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":true,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":267563941,\"identity\":\"d9517ec8-468d-4d27-95bb-e539bed661e9\",\"order_by\":0,\"name\":\"Jinyv Wang\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Shanghai Changzheng Hospital\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Jinyv\",\"middleName\":\"\",\"lastName\":\"Wang\",\"suffix\":\"\"},{\"id\":267563942,\"identity\":\"a4cd4431-29e4-431c-af36-ff871a8ab601\",\"order_by\":1,\"name\":\"Chen Yan\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Shanghai Changzheng Hospital\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Chen\",\"middleName\":\"\",\"lastName\":\"Yan\",\"suffix\":\"\"},{\"id\":267563943,\"identity\":\"ab32753d-4552-47b4-a85a-115e1b52a3c8\",\"order_by\":2,\"name\":\"Linhui Han\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Shanghai Changzheng Hospital\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Linhui\",\"middleName\":\"\",\"lastName\":\"Han\",\"suffix\":\"\"},{\"id\":267563944,\"identity\":\"d4189c76-b17e-4e7b-b49e-eea581881368\",\"order_by\":3,\"name\":\"YiJuan Lu\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Shanghai Changzheng Hospital\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"YiJuan\",\"middleName\":\"\",\"lastName\":\"Lu\",\"suffix\":\"\"},{\"id\":267563945,\"identity\":\"dcd94469-255b-470e-bd04-ebb2241311ac\",\"order_by\":4,\"name\":\"JingChuan Sun\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Shanghai Changzheng Hospital\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"JingChuan\",\"middleName\":\"\",\"lastName\":\"Sun\",\"suffix\":\"\"},{\"id\":267563946,\"identity\":\"474bbeab-7155-4469-8881-76b0faaf89a0\",\"order_by\":5,\"name\":\"Jiangang Shi\",\"email\":\"data:image/png;base64,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\",\"orcid\":\"\",\"institution\":\"Shanghai Changzheng Hospital\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"Jiangang\",\"middleName\":\"\",\"lastName\":\"Shi\",\"suffix\":\"\"},{\"id\":267563947,\"identity\":\"f8b83301-ab0e-4e24-8602-6e547d3bcde0\",\"order_by\":6,\"name\":\"Kaiqiang Sun\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Shanghai Changzheng Hospital\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Kaiqiang\",\"middleName\":\"\",\"lastName\":\"Sun\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2024-01-14 10:44:12\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-3863003/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-3863003/v1\",\"draftVersion\":[],\"editorialEvents\":[],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":49812922,\"identity\":\"647a146b-c59b-4ac3-b8c0-82d06e4c4f5f\",\"added_by\":\"auto\",\"created_at\":\"2024-01-18 12:37:36\",\"extension\":\"png\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":160710,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eOverall flow chart of this study\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Fig1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-3863003/v1/4081502c6fcf2f189255274b.png\"},{\"id\":49812921,\"identity\":\"ba82da13-ec0f-4bb4-9c23-6cc59246d6e4\",\"added_by\":\"auto\",\"created_at\":\"2024-01-18 12:37:36\",\"extension\":\"png\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":252925,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eScatter plot of causality between intestinal flora and radiculopathy. Each panel performed five different-colored methods (bottom right of the figure) to show the correlation between the SNP's effect on radiculopathy and each gut microbiome. (A) SNP effect on Betaproteobacteria and radiculopathy. (B) SNP effect on Allisonella and radiculopathy. (C) SNP effect on Terrisporobacter and radiculopathy. (D) SNP effect on Veillonella and radiculopathy. (E) SNP effect on LachnospiraceaeNC2004group and radiculopathy. (F) SNP effect on Marvinbryantia and radiculopathy. (G) SNP effect on Olsenella and radiculopathy. (H) SNP effect on Oxalobacter and radiculopathy. SNP, Single Nucleotide Polymorphism.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Fig2.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-3863003/v1/c9801c7fddbbf1d966b61078.png\"},{\"id\":49812923,\"identity\":\"8f194587-f9d6-47d0-bbb8-7672669a90b4\",\"added_by\":\"auto\",\"created_at\":\"2024-01-18 12:37:36\",\"extension\":\"png\",\"order_by\":3,\"title\":\"Figure 3\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":62748,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eMR results and forest plot of gut microbiome with a causal relationship to radiculopathy. IVW, Inverse variance weighting; WM, weighted median; OR, odds ratio; CI, confidence interval.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Fig3.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-3863003/v1/edf24b8533cfb4a2cab10c6e.png\"},{\"id\":53968994,\"identity\":\"72c528cf-8d29-4a39-8f62-72f5076748cc\",\"added_by\":\"auto\",\"created_at\":\"2024-04-02 20:25:11\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":683680,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-3863003/v1/89f6dbf6-a646-4fcc-bca1-759a973ef6c4.pdf\"},{\"id\":49812924,\"identity\":\"85a8f929-3e48-4801-899c-ff62e2548a95\",\"added_by\":\"auto\",\"created_at\":\"2024-01-18 12:37:36\",\"extension\":\"xlsx\",\"order_by\":1,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":11057,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"file.xlsx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-3863003/v1/2b706576d8134600330e0ec4.xlsx\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"Gut microbiota and radiculopathy: a bidirectional two-sample Mendelian randomization study\",\"fulltext\":[{\"header\":\"Introduction\",\"content\":\"\\u003cp\\u003eRadiculopathy is one of the major spine degeneration-related diseases that affects or damages the nerve roots, leading to abnormal sensation and movement of the affected nerve roots. This dysfunction is often caused by a herniated disc, which compresses the nerve roots and prevents them from functioning normally. The symptoms of radiculopathy are diverse, yet the most common presentations include pain and focal neurological disturbances. Furthermore, individuals may experience paraesthesia, numbness, muscle weakness, atrophy, and other related symptoms[1].\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eThe human gut is the home to trillions of bacterial cells, which play a pivotal role in regulating host metabolites, vitamin synthesis, and immune homeostasis. These bacteria have also been linked to the development of various diseases, highlighting their significance in maintaining overall health. \\u0026nbsp; Recent studies have reported a causal relationship between gut microbes and disc degeneration as well as other spinal degenerative diseases[2-4]. Although it has been reported in the literature that it is associated with radiculopathy, the causal relationship has not been clarified.\\u003c/p\\u003e\\n\\u003cp\\u003eMendelian randomization is a new statistical technique that employs genetic variation as a tool for detecting and quantifying causal relationships[5].This approach offers the advantage of overcoming the potential impact of confounding and reverse causality by utilizing genetic markers that are randomly allocated at conception and remain stable throughout life. \\u0026nbsp;Therefoe, In this study,we conducted a causal analysis of radiculopathy by utilizing aggregated data of gut microbiome.Our objective was to identify intestinal microbiome taxa that may significantly impact the development of radiculopathy,thereby providing a foundation for subsequent treatment and diagnosis.\\u003c/p\\u003e\"},{\"header\":\"2. Materials and methods \",\"content\":\"\\u003cp\\u003e2.1Ethics approval statement\\u003c/p\\u003e\\n\\u003cp\\u003eEach GWAS involved in this study was ethically approved by the respective institutions.\\u003c/p\\u003e\\n\\u003cp\\u003e2.2Study design\\u003c/p\\u003e\\n\\u003cp\\u003eWe used two-sample Mendelian randomization to evaluate the causal effect of gut microbiota on radiculopathy with single nucleotide polymorphisms (SNPS) associated with various gut microbes as IVs. Our findings were presented in accordance with the STROBE-MR guidelines. MR studies require the fulfillment of three assumptions: (1) a robust correlation between the instrumental variable (IV) and the exposure, (2) the IVs are independent of confounding factors, and (3) the IVs solely influence the outcome through their association with the exposure.\\u003c/p\\u003e\\n\\u003cp\\u003e2.3Exposure data of gut microbiota\\u003c/p\\u003e\\n\\u003cp\\u003eSummary data on gut microbes were obtained from a large-scale GWAS study conducted by MiBioGen. This study collected 24 cohorts consisting of 18,340 participants, mainly from European and American countries. \\u0026nbsp;The host genotype and 16S fecal microbiome rRNA gene sequencing profiles of participants were studied, and a total of 211 microbial taxa were analyzed by quantitative trait loci.[6]\\u003c/p\\u003e\\n\\u003cp\\u003e2.4Outcome data of radiculopathy\\u003c/p\\u003e\\n\\u003cp\\u003eThe data for radiculopathy is derived from the FinnGen database of Genetic Statistics published in May 2023, version DF9 .FinnGen is a large public-private partnership aiming to collect and analyse genome and health data from 500,000 Finnish biobank participants. In the DF9 version, 377,277 samples (210 870 women and 166 407 men) were collected, which yielded 20,175,454 single-nucleotide polymorphisms (SNPS) for analysis after adjustment for age, sex, and genotyping batch.[7]\\u003c/p\\u003e\\n\\u003cp\\u003e2.5Genetic instruments selection and harmonization\\u003c/p\\u003e\\n\\u003cp\\u003eMendelian randomization (MR) employs genetic variation as an instrumental variable (IV) to infer causal relationships between exposure and outcome. To ensure confidence in the findings, the selection of IVs must adhere to three principles: (1) IVs must be independent of confounding factors; (2) IVs must be strongly correlated with exposure factors; and (3) IVs must not be associated with outcome variables except through their correlation with the exposure. As the number of IVs obtained under the strict threshold (P \\u0026lt; 5 \\u0026times; 10\\u0026minus;8) is limited, we employed a more comprehensive threshold (P \\u0026lt; 1 \\u0026times; 10\\u0026minus;5) to acquire a larger number of IVs. Additionally, to ensure the independence of each IV, SNPs with a threshold of r2 \\u0026lt; 0.001 within a 500 kb window size were pruned to mitigate linkage disequilibrium (LD). The strength of each SNP was represented by the F statistic, which was calculated using the formula:\\u003c/p\\u003e\\n\\u003cp\\u003eF = R2 \\u0026times; (N \\u0026minus; 1 \\u0026minus; K)/ [(1 \\u0026ndash; R2)\\u0026times; K]\\u003c/p\\u003e\\n\\u003cp\\u003eIn this formula, R2 is the proportion of variability explained by each SNP, N is the GWAS sample size, and k is the number of SNPS. A statistic of F greater than 10 indicates that there is no convincing evidence of instrumental bias.\\u003c/p\\u003e\\n\\u003cp\\u003e2.6Statistical analysis\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eIn order to explore the causal relationship between intestinal microorganisms and radiculopathy, inverse variance weighting (IVW) method was used as the main analysis method to determine the causal relationship (P\\u0026lt;0.05), and MR-Egger, weighted median, simple model and weighted model were used as supplementary analysis methods. The Cochran Q test was utilized to assess the heterogeneity among SNPs. A P-value exceeding 0.05 denoted a low likelihood of heterogeneity among SNPs. To evaluate potential pleotropic effects, MR-Egger regression analysis was conducted to ensure the independence of instrumental variables from confounders. Additionally, MR Pleotropic residuals and outliers (MR-PRESSO) tests were performed. Odds ratios (ORs) and 95% confidence intervals (CIs) were utilized to represent the relationship between gut microbiota and radiculopathy(Fig1).\\u003c/p\\u003e\\n\\u003cp\\u003eAll statistical analyses were conducted using R version 4.2.3. The \\u0026ldquo;Two-sample MR\\u0026rdquo; and \\u0026ldquo;MRPRESSO\\u0026rdquo; R packages were used for statistical analysis in R version 4.2.3. The statistical threshold for causal effect evidence was set at P \\u0026lt; 0.05.\\u003c/p\\u003e\"},{\"header\":\"3. Results\",\"content\":\"\\u003cp\\u003e3.1. The selection of IVs related to gut microbiome\\u003c/p\\u003e\\n\\u003cp\\u003eAfter taking into account the linkage disequilibrium effect, palindromic effect and weak tool bias, 2350 SNPS from 191 bacterial species were taken as IVs. These taxa include 9 phyla (106 SNPs), 16 classes (189 SNPs), 20 orders (231 SNPs), 35 families (413 SNPs), and 131 genera (1411 SNPs). For further analysis, we collected key SNP data including effector alleles, beta, standard error (SE), and P-values. The F-statistics of IVs were all generally greater than 10, indicating no evidence of weak instrument bias.\\u003c/p\\u003e\\n\\u003cp\\u003e3.2 Causal effects of gut microbiota on radiculopathy\\u003c/p\\u003e\\n\\u003cp\\u003eA total of 8 causal associations from gut microbiota features (1 class and 7 genera) to radiculopathy traits were identified by the IVW method. As shown in Figure 3, IVW analysis showed a causal relationship between Class Betaproteobacteria (OR: 0.76,95%CI: 0.59-0.99,P=0.042), genus Allisonella(OR: 1.15,95%CI: 1.03-1.29,P=0.010), genus Terrisporobacter(OR: 1.31,95%CI: 1.05-1.62,P=0.014), genus Veillonella(OR: 0.73,95%CI: 0,57-0.94,P=0.015), genus LachnospiraceaeNC2004group(OR: 0.76,95%CI: 0,64-0.90,P=0.002), genus Marvinbryantia(OR: 1.28,95%CI: 1.04-1.56,P=0.015), genus Olsenella(OR: 0.87,95%CI: 0.76-0.89,P=0.027), genus Oxalobacter(OR: 1.18,95%CI: 1.05-1.32,P=0.003)and radiculopathy(Fig2,3,Table1).According to the Cochrane Q and MR-Egger tests, no heterogeneity was observed, and the analysis results of MR-Egger and MRPRESSO global tests indicated the absence of horizontal pleiotropy(Supplementary material 1). In reverse MR Analysis, we found that radiculopathy had no significant causal effect on these eight gut microbiota(Supplementary material 2).\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eTable 1MR estimates for the association between gut microbiota and radiculopathy. MR method, method used for Mendelian randomization analysis; nSNP, the number of SNPs selected for MR analysis; OR, odds ratio; OR-low, the lower limit of the confidence interval of OR; OR-high, the upper limit of the confidence interval of OR.\\u003c/p\\u003e\\n\\u003ctable border=\\\"0\\\" cellspacing=\\\"0\\\" cellpadding=\\\"0\\\"\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"25.72463768115942%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eExposure\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"18.840579710144926%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eMR method\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"5.797101449275362%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003enSNP\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.768115942028986%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eOR\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.768115942028986%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eOR-low\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.768115942028986%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eOR-high\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"8.333333333333334%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eP-value\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"25.72463768115942%\\\"\\u003e\\n \\u003cp\\u003eclass.Betaproteobacteria\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"18.840579710144926%\\\"\\u003e\\n \\u003cp\\u003eMR Egger\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"5.797101449275362%\\\"\\u003e\\n \\u003cp\\u003e10\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.768115942028986%\\\"\\u003e\\n \\u003cp\\u003e1.3716452\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.768115942028986%\\\"\\u003e\\n \\u003cp\\u003e0.654658852\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.768115942028986%\\\"\\u003e\\n \\u003cp\\u003e2.873879348\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"8.333333333333334%\\\"\\u003e\\n \\u003cp\\u003e0.43\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"25.72463768115942%\\\"\\u003e\\n \\u003cp\\u003eclass.Betaproteobacteria\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"18.840579710144926%\\\"\\u003e\\n \\u003cp\\u003eWeighted median\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"5.797101449275362%\\\"\\u003e\\n \\u003cp\\u003e10\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.768115942028986%\\\"\\u003e\\n \\u003cp\\u003e0.662552948\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.768115942028986%\\\"\\u003e\\n \\u003cp\\u003e0.472400455\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.768115942028986%\\\"\\u003e\\n \\u003cp\\u003e0.929246372\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"8.333333333333334%\\\"\\u003e\\n \\u003cp\\u003e1.70E-02\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"25.72463768115942%\\\"\\u003e\\n \\u003cp\\u003eclass.Betaproteobacteria\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"18.840579710144926%\\\"\\u003e\\n \\u003cp\\u003eInverse variance weighted\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"5.797101449275362%\\\"\\u003e\\n \\u003cp\\u003e10\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.768115942028986%\\\"\\u003e\\n \\u003cp\\u003e0.769773901\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.768115942028986%\\\"\\u003e\\n \\u003cp\\u003e0.597486854\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.768115942028986%\\\"\\u003e\\n \\u003cp\\u003e0.991740411\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"8.333333333333334%\\\"\\u003e\\n \\u003cp\\u003e4.30E-02\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"25.72463768115942%\\\"\\u003e\\n \\u003cp\\u003eclass.Betaproteobacteria\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"18.840579710144926%\\\"\\u003e\\n \\u003cp\\u003eSimple mode\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"5.797101449275362%\\\"\\u003e\\n \\u003cp\\u003e10\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.768115942028986%\\\"\\u003e\\n \\u003cp\\u003e0.57721211\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.768115942028986%\\\"\\u003e\\n \\u003cp\\u003e0.318941787\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.768115942028986%\\\"\\u003e\\n \\u003cp\\u003e1.044622666\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"8.333333333333334%\\\"\\u003e\\n \\u003cp\\u003e0.1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"25.72463768115942%\\\"\\u003e\\n \\u003cp\\u003eclass.Betaproteobacteria\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"18.840579710144926%\\\"\\u003e\\n \\u003cp\\u003eWeighted mode\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"5.797101449275362%\\\"\\u003e\\n \\u003cp\\u003e10\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.768115942028986%\\\"\\u003e\\n \\u003cp\\u003e0.594262983\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.768115942028986%\\\"\\u003e\\n \\u003cp\\u003e0.334050731\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.768115942028986%\\\"\\u003e\\n \\u003cp\\u003e1.057170246\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"8.333333333333334%\\\"\\u003e\\n \\u003cp\\u003e0.11\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"25.72463768115942%\\\"\\u003e\\n \\u003cp\\u003egenus.Allisonella\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"18.840579710144926%\\\"\\u003e\\n \\u003cp\\u003eMR Egger\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"5.797101449275362%\\\"\\u003e\\n \\u003cp\\u003e8\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.768115942028986%\\\"\\u003e\\n \\u003cp\\u003e1.753657305\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.768115942028986%\\\"\\u003e\\n \\u003cp\\u003e0.82553478\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.768115942028986%\\\"\\u003e\\n \\u003cp\\u003e3.725238496\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"8.333333333333334%\\\"\\u003e\\n \\u003cp\\u003e0.19\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"25.72463768115942%\\\"\\u003e\\n \\u003cp\\u003egenus.Allisonella\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"18.840579710144926%\\\"\\u003e\\n \\u003cp\\u003eWeighted median\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"5.797101449275362%\\\"\\u003e\\n \\u003cp\\u003e8\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.768115942028986%\\\"\\u003e\\n \\u003cp\\u003e1.158928674\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.768115942028986%\\\"\\u003e\\n \\u003cp\\u003e1.000538528\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.768115942028986%\\\"\\u003e\\n \\u003cp\\u003e1.342392756\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"8.333333333333334%\\\"\\u003e\\n \\u003cp\\u003e4.90E-02\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"25.72463768115942%\\\"\\u003e\\n \\u003cp\\u003egenus.Allisonella\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"18.840579710144926%\\\"\\u003e\\n \\u003cp\\u003eInverse variance weighted\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"5.797101449275362%\\\"\\u003e\\n \\u003cp\\u003e8\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.768115942028986%\\\"\\u003e\\n \\u003cp\\u003e1.156965338\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.768115942028986%\\\"\\u003e\\n \\u003cp\\u003e1.035116314\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.768115942028986%\\\"\\u003e\\n \\u003cp\\u003e1.293157857\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"8.333333333333334%\\\"\\u003e\\n \\u003cp\\u003e1.00E-02\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"25.72463768115942%\\\"\\u003e\\n \\u003cp\\u003egenus.Allisonella\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"18.840579710144926%\\\"\\u003e\\n \\u003cp\\u003eSimple mode\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"5.797101449275362%\\\"\\u003e\\n \\u003cp\\u003e8\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.768115942028986%\\\"\\u003e\\n \\u003cp\\u003e1.174911138\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.768115942028986%\\\"\\u003e\\n \\u003cp\\u003e0.942218544\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.768115942028986%\\\"\\u003e\\n \\u003cp\\u003e1.465070064\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"8.333333333333334%\\\"\\u003e\\n \\u003cp\\u003e0.2\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"25.72463768115942%\\\"\\u003e\\n \\u003cp\\u003egenus.Allisonella\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"18.840579710144926%\\\"\\u003e\\n \\u003cp\\u003eWeighted mode\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"5.797101449275362%\\\"\\u003e\\n \\u003cp\\u003e8\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.768115942028986%\\\"\\u003e\\n \\u003cp\\u003e1.163820865\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.768115942028986%\\\"\\u003e\\n \\u003cp\\u003e0.944194345\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.768115942028986%\\\"\\u003e\\n \\u003cp\\u003e1.434534123\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"8.333333333333334%\\\"\\u003e\\n \\u003cp\\u003e0.2\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"25.72463768115942%\\\"\\u003e\\n \\u003cp\\u003egenus.Terrisporobacter\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"18.840579710144926%\\\"\\u003e\\n \\u003cp\\u003eMR Egger\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"5.797101449275362%\\\"\\u003e\\n \\u003cp\\u003e5\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.768115942028986%\\\"\\u003e\\n \\u003cp\\u003e1.862941078\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.768115942028986%\\\"\\u003e\\n \\u003cp\\u003e0.964475477\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.768115942028986%\\\"\\u003e\\n \\u003cp\\u003e3.598380203\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"8.333333333333334%\\\"\\u003e\\n \\u003cp\\u003e0.16\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"25.72463768115942%\\\"\\u003e\\n \\u003cp\\u003egenus.Terrisporobacter\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"18.840579710144926%\\\"\\u003e\\n \\u003cp\\u003eWeighted median\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"5.797101449275362%\\\"\\u003e\\n \\u003cp\\u003e5\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.768115942028986%\\\"\\u003e\\n \\u003cp\\u003e1.370171923\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.768115942028986%\\\"\\u003e\\n \\u003cp\\u003e1.027816565\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.768115942028986%\\\"\\u003e\\n \\u003cp\\u003e1.826562409\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"8.333333333333334%\\\"\\u003e\\n \\u003cp\\u003e3.20E-02\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"25.72463768115942%\\\"\\u003e\\n \\u003cp\\u003egenus.Terrisporobacter\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"18.840579710144926%\\\"\\u003e\\n \\u003cp\\u003eInverse variance weighted\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"5.797101449275362%\\\"\\u003e\\n \\u003cp\\u003e5\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.768115942028986%\\\"\\u003e\\n \\u003cp\\u003e1.310734015\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.768115942028986%\\\"\\u003e\\n \\u003cp\\u003e1.054442104\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.768115942028986%\\\"\\u003e\\n \\u003cp\\u003e1.629320046\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"8.333333333333334%\\\"\\u003e\\n \\u003cp\\u003e1.50E-02\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"25.72463768115942%\\\"\\u003e\\n \\u003cp\\u003egenus.Terrisporobacter\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"18.840579710144926%\\\"\\u003e\\n \\u003cp\\u003eSimple mode\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"5.797101449275362%\\\"\\u003e\\n \\u003cp\\u003e5\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.768115942028986%\\\"\\u003e\\n \\u003cp\\u003e1.415014185\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.768115942028986%\\\"\\u003e\\n \\u003cp\\u003e0.954287425\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.768115942028986%\\\"\\u003e\\n \\u003cp\\u003e2.098178276\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"8.333333333333334%\\\"\\u003e\\n \\u003cp\\u003e0.16\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"25.72463768115942%\\\"\\u003e\\n \\u003cp\\u003egenus.Terrisporobacter\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"18.840579710144926%\\\"\\u003e\\n \\u003cp\\u003eWeighted mode\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"5.797101449275362%\\\"\\u003e\\n \\u003cp\\u003e5\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.768115942028986%\\\"\\u003e\\n \\u003cp\\u003e1.377822422\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.768115942028986%\\\"\\u003e\\n \\u003cp\\u003e0.922765796\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.768115942028986%\\\"\\u003e\\n \\u003cp\\u003e2.057287596\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"8.333333333333334%\\\"\\u003e\\n \\u003cp\\u003e0.19\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"25.72463768115942%\\\"\\u003e\\n \\u003cp\\u003egenus.Veillonella\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"18.840579710144926%\\\"\\u003e\\n \\u003cp\\u003eMR Egger\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"5.797101449275362%\\\"\\u003e\\n \\u003cp\\u003e5\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.768115942028986%\\\"\\u003e\\n \\u003cp\\u003e1.161942416\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.768115942028986%\\\"\\u003e\\n \\u003cp\\u003e0.003856414\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.768115942028986%\\\"\\u003e\\n \\u003cp\\u003e350.0947096\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"8.333333333333334%\\\"\\u003e\\n \\u003cp\\u003e0.96\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"25.72463768115942%\\\"\\u003e\\n \\u003cp\\u003egenus.Veillonella\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"18.840579710144926%\\\"\\u003e\\n \\u003cp\\u003eWeighted median\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"5.797101449275362%\\\"\\u003e\\n \\u003cp\\u003e5\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.768115942028986%\\\"\\u003e\\n \\u003cp\\u003e0.765118042\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.768115942028986%\\\"\\u003e\\n \\u003cp\\u003e0.560789815\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.768115942028986%\\\"\\u003e\\n \\u003cp\\u003e1.043894882\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"8.333333333333334%\\\"\\u003e\\n \\u003cp\\u003e0.09\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"25.72463768115942%\\\"\\u003e\\n \\u003cp\\u003egenus.Veillonella\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"18.840579710144926%\\\"\\u003e\\n \\u003cp\\u003eInverse variance weighted\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"5.797101449275362%\\\"\\u003e\\n \\u003cp\\u003e5\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.768115942028986%\\\"\\u003e\\n \\u003cp\\u003e0.736895008\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.768115942028986%\\\"\\u003e\\n \\u003cp\\u003e0.575893069\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.768115942028986%\\\"\\u003e\\n \\u003cp\\u003e0.942908124\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"8.333333333333334%\\\"\\u003e\\n \\u003cp\\u003e1.50E-02\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"25.72463768115942%\\\"\\u003e\\n \\u003cp\\u003egenus.Veillonella\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"18.840579710144926%\\\"\\u003e\\n \\u003cp\\u003eSimple mode\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"5.797101449275362%\\\"\\u003e\\n \\u003cp\\u003e5\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.768115942028986%\\\"\\u003e\\n \\u003cp\\u003e0.820293617\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.768115942028986%\\\"\\u003e\\n \\u003cp\\u003e0.531614769\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.768115942028986%\\\"\\u003e\\n \\u003cp\\u003e1.265731611\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"8.333333333333334%\\\"\\u003e\\n \\u003cp\\u003e0.42\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"25.72463768115942%\\\"\\u003e\\n \\u003cp\\u003egenus.Veillonella\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"18.840579710144926%\\\"\\u003e\\n \\u003cp\\u003eWeighted mode\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"5.797101449275362%\\\"\\u003e\\n \\u003cp\\u003e5\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.768115942028986%\\\"\\u003e\\n \\u003cp\\u003e0.838305598\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.768115942028986%\\\"\\u003e\\n \\u003cp\\u003e0.550829514\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.768115942028986%\\\"\\u003e\\n \\u003cp\\u003e1.27581449\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"8.333333333333334%\\\"\\u003e\\n \\u003cp\\u003e0.46\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"25.72463768115942%\\\"\\u003e\\n \\u003cp\\u003egenus.LachnospiraceaeNC2004group\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"18.840579710144926%\\\"\\u003e\\n \\u003cp\\u003eMR Egger\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"5.797101449275362%\\\"\\u003e\\n \\u003cp\\u003e9\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.768115942028986%\\\"\\u003e\\n \\u003cp\\u003e0.574322148\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.768115942028986%\\\"\\u003e\\n \\u003cp\\u003e0.28600439\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.768115942028986%\\\"\\u003e\\n \\u003cp\\u003e1.153289744\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"8.333333333333334%\\\"\\u003e\\n \\u003cp\\u003e0.16\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"25.72463768115942%\\\"\\u003e\\n \\u003cp\\u003egenus.LachnospiraceaeNC2004group\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"18.840579710144926%\\\"\\u003e\\n \\u003cp\\u003eWeighted median\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"5.797101449275362%\\\"\\u003e\\n \\u003cp\\u003e9\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.768115942028986%\\\"\\u003e\\n \\u003cp\\u003e0.825097671\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.768115942028986%\\\"\\u003e\\n \\u003cp\\u003e0.660980939\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.768115942028986%\\\"\\u003e\\n \\u003cp\\u003e1.029963388\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"8.333333333333334%\\\"\\u003e\\n \\u003cp\\u003e0.09\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"25.72463768115942%\\\"\\u003e\\n \\u003cp\\u003egenus.LachnospiraceaeNC2004group\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"18.840579710144926%\\\"\\u003e\\n \\u003cp\\u003eInverse variance weighted\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"5.797101449275362%\\\"\\u003e\\n \\u003cp\\u003e9\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.768115942028986%\\\"\\u003e\\n \\u003cp\\u003e0.767976785\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.768115942028986%\\\"\\u003e\\n \\u003cp\\u003e0.648864568\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.768115942028986%\\\"\\u003e\\n \\u003cp\\u003e0.908954459\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"8.333333333333334%\\\"\\u003e\\n \\u003cp\\u003e2.10E-03\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"25.72463768115942%\\\"\\u003e\\n \\u003cp\\u003egenus.LachnospiraceaeNC2004group\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"18.840579710144926%\\\"\\u003e\\n \\u003cp\\u003eSimple mode\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"5.797101449275362%\\\"\\u003e\\n \\u003cp\\u003e9\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.768115942028986%\\\"\\u003e\\n \\u003cp\\u003e0.846177362\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.768115942028986%\\\"\\u003e\\n \\u003cp\\u003e0.575894917\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.768115942028986%\\\"\\u003e\\n \\u003cp\\u003e1.24331038\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"8.333333333333334%\\\"\\u003e\\n \\u003cp\\u003e0.42\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"25.72463768115942%\\\"\\u003e\\n \\u003cp\\u003egenus.LachnospiraceaeNC2004group\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"18.840579710144926%\\\"\\u003e\\n \\u003cp\\u003eWeighted mode\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"5.797101449275362%\\\"\\u003e\\n \\u003cp\\u003e9\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.768115942028986%\\\"\\u003e\\n \\u003cp\\u003e0.851605658\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.768115942028986%\\\"\\u003e\\n \\u003cp\\u003e0.601203295\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.768115942028986%\\\"\\u003e\\n \\u003cp\\u003e1.2063011\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"8.333333333333334%\\\"\\u003e\\n \\u003cp\\u003e0.39\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"25.72463768115942%\\\"\\u003e\\n \\u003cp\\u003egenus.Marvinbryantia\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"18.840579710144926%\\\"\\u003e\\n \\u003cp\\u003eMR Egger\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"5.797101449275362%\\\"\\u003e\\n \\u003cp\\u003e11\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.768115942028986%\\\"\\u003e\\n \\u003cp\\u003e1.220795266\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.768115942028986%\\\"\\u003e\\n \\u003cp\\u003e0.555326936\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.768115942028986%\\\"\\u003e\\n \\u003cp\\u003e2.683718333\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"8.333333333333334%\\\"\\u003e\\n \\u003cp\\u003e0.63\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"25.72463768115942%\\\"\\u003e\\n \\u003cp\\u003egenus.Marvinbryantia\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"18.840579710144926%\\\"\\u003e\\n \\u003cp\\u003eWeighted median\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"5.797101449275362%\\\"\\u003e\\n \\u003cp\\u003e11\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.768115942028986%\\\"\\u003e\\n \\u003cp\\u003e1.090269256\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.768115942028986%\\\"\\u003e\\n \\u003cp\\u003e0.844693683\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.768115942028986%\\\"\\u003e\\n \\u003cp\\u003e1.407240369\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"8.333333333333334%\\\"\\u003e\\n \\u003cp\\u003e0.51\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"25.72463768115942%\\\"\\u003e\\n \\u003cp\\u003egenus.Marvinbryantia\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"18.840579710144926%\\\"\\u003e\\n \\u003cp\\u003eInverse variance weighted\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"5.797101449275362%\\\"\\u003e\\n \\u003cp\\u003e11\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.768115942028986%\\\"\\u003e\\n \\u003cp\\u003e1.28059704\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.768115942028986%\\\"\\u003e\\n \\u003cp\\u003e1.04856999\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.768115942028986%\\\"\\u003e\\n \\u003cp\\u003e1.563966922\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"8.333333333333334%\\\"\\u003e\\n \\u003cp\\u003e1.50E-02\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"25.72463768115942%\\\"\\u003e\\n \\u003cp\\u003egenus.Marvinbryantia\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"18.840579710144926%\\\"\\u003e\\n \\u003cp\\u003eSimple mode\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"5.797101449275362%\\\"\\u003e\\n \\u003cp\\u003e11\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.768115942028986%\\\"\\u003e\\n \\u003cp\\u003e1.078480655\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.768115942028986%\\\"\\u003e\\n \\u003cp\\u003e0.712485157\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.768115942028986%\\\"\\u003e\\n \\u003cp\\u003e1.632483866\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"8.333333333333334%\\\"\\u003e\\n \\u003cp\\u003e0.73\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"25.72463768115942%\\\"\\u003e\\n \\u003cp\\u003egenus.Marvinbryantia\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"18.840579710144926%\\\"\\u003e\\n \\u003cp\\u003eWeighted mode\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"5.797101449275362%\\\"\\u003e\\n \\u003cp\\u003e11\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.768115942028986%\\\"\\u003e\\n \\u003cp\\u003e1.053208689\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.768115942028986%\\\"\\u003e\\n \\u003cp\\u003e0.759937479\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.768115942028986%\\\"\\u003e\\n \\u003cp\\u003e1.459657632\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"8.333333333333334%\\\"\\u003e\\n \\u003cp\\u003e0.76\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"25.72463768115942%\\\"\\u003e\\n \\u003cp\\u003egenus.Olsenella\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"18.840579710144926%\\\"\\u003e\\n \\u003cp\\u003eMR Egger\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"5.797101449275362%\\\"\\u003e\\n \\u003cp\\u003e10\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.768115942028986%\\\"\\u003e\\n \\u003cp\\u003e1.026965168\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.768115942028986%\\\"\\u003e\\n \\u003cp\\u003e0.686918438\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.768115942028986%\\\"\\u003e\\n \\u003cp\\u003e1.535345971\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"8.333333333333334%\\\"\\u003e\\n \\u003cp\\u003e0.9\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"25.72463768115942%\\\"\\u003e\\n \\u003cp\\u003egenus.Olsenella\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"18.840579710144926%\\\"\\u003e\\n \\u003cp\\u003eWeighted median\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"5.797101449275362%\\\"\\u003e\\n \\u003cp\\u003e10\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.768115942028986%\\\"\\u003e\\n \\u003cp\\u003e0.875200641\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.768115942028986%\\\"\\u003e\\n \\u003cp\\u003e0.747004671\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.768115942028986%\\\"\\u003e\\n \\u003cp\\u003e1.02539675\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"8.333333333333334%\\\"\\u003e\\n \\u003cp\\u003e0.1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"25.72463768115942%\\\"\\u003e\\n \\u003cp\\u003egenus.Olsenella\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"18.840579710144926%\\\"\\u003e\\n \\u003cp\\u003eInverse variance weighted\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"5.797101449275362%\\\"\\u003e\\n \\u003cp\\u003e10\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.768115942028986%\\\"\\u003e\\n \\u003cp\\u003e0.870179464\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.768115942028986%\\\"\\u003e\\n \\u003cp\\u003e0.768930403\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.768115942028986%\\\"\\u003e\\n \\u003cp\\u003e0.984760515\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"8.333333333333334%\\\"\\u003e\\n \\u003cp\\u003e2.80E-02\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"25.72463768115942%\\\"\\u003e\\n \\u003cp\\u003egenus.Olsenella\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"18.840579710144926%\\\"\\u003e\\n \\u003cp\\u003eSimple mode\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"5.797101449275362%\\\"\\u003e\\n \\u003cp\\u003e10\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.768115942028986%\\\"\\u003e\\n \\u003cp\\u003e0.823303749\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.768115942028986%\\\"\\u003e\\n \\u003cp\\u003e0.641753618\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.768115942028986%\\\"\\u003e\\n \\u003cp\\u003e1.056213856\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"8.333333333333334%\\\"\\u003e\\n \\u003cp\\u003e0.16\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"25.72463768115942%\\\"\\u003e\\n \\u003cp\\u003egenus.Olsenella\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"18.840579710144926%\\\"\\u003e\\n \\u003cp\\u003eWeighted mode\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"5.797101449275362%\\\"\\u003e\\n \\u003cp\\u003e10\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.768115942028986%\\\"\\u003e\\n \\u003cp\\u003e0.892767231\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.768115942028986%\\\"\\u003e\\n \\u003cp\\u003e0.716400419\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.768115942028986%\\\"\\u003e\\n \\u003cp\\u003e1.112552851\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"8.333333333333334%\\\"\\u003e\\n \\u003cp\\u003e0.34\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"25.72463768115942%\\\"\\u003e\\n \\u003cp\\u003egenus.Oxalobacter\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"18.840579710144926%\\\"\\u003e\\n \\u003cp\\u003eMR Egger\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"5.797101449275362%\\\"\\u003e\\n \\u003cp\\u003e11\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.768115942028986%\\\"\\u003e\\n \\u003cp\\u003e1.366522791\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.768115942028986%\\\"\\u003e\\n \\u003cp\\u003e0.786470664\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.768115942028986%\\\"\\u003e\\n \\u003cp\\u003e2.374385497\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"8.333333333333334%\\\"\\u003e\\n \\u003cp\\u003e0.3\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"25.72463768115942%\\\"\\u003e\\n \\u003cp\\u003egenus.Oxalobacter\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"18.840579710144926%\\\"\\u003e\\n \\u003cp\\u003eWeighted median\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"5.797101449275362%\\\"\\u003e\\n \\u003cp\\u003e11\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.768115942028986%\\\"\\u003e\\n \\u003cp\\u003e1.167015992\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.768115942028986%\\\"\\u003e\\n \\u003cp\\u003e1.000132881\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.768115942028986%\\\"\\u003e\\n \\u003cp\\u003e1.361745375\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"8.333333333333334%\\\"\\u003e\\n \\u003cp\\u003e5.00E-02\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"25.72463768115942%\\\"\\u003e\\n \\u003cp\\u003egenus.Oxalobacter\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"18.840579710144926%\\\"\\u003e\\n \\u003cp\\u003eInverse variance weighted\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"5.797101449275362%\\\"\\u003e\\n \\u003cp\\u003e11\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.768115942028986%\\\"\\u003e\\n \\u003cp\\u003e1.184313631\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.768115942028986%\\\"\\u003e\\n \\u003cp\\u003e1.057371854\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.768115942028986%\\\"\\u003e\\n \\u003cp\\u003e1.326495282\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"8.333333333333334%\\\"\\u003e\\n \\u003cp\\u003e3.50E-03\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"25.72463768115942%\\\"\\u003e\\n \\u003cp\\u003egenus.Oxalobacter\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"18.840579710144926%\\\"\\u003e\\n \\u003cp\\u003eSimple mode\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"5.797101449275362%\\\"\\u003e\\n \\u003cp\\u003e11\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.768115942028986%\\\"\\u003e\\n \\u003cp\\u003e1.138222141\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.768115942028986%\\\"\\u003e\\n \\u003cp\\u003e0.8940882\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.768115942028986%\\\"\\u003e\\n \\u003cp\\u003e1.449017716\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"8.333333333333334%\\\"\\u003e\\n \\u003cp\\u003e0.32\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"25.72463768115942%\\\"\\u003e\\n \\u003cp\\u003egenus.Oxalobacter\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"18.840579710144926%\\\"\\u003e\\n \\u003cp\\u003eWeighted mode\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"5.797101449275362%\\\"\\u003e\\n \\u003cp\\u003e11\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.768115942028986%\\\"\\u003e\\n \\u003cp\\u003e1.144322948\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.768115942028986%\\\"\\u003e\\n \\u003cp\\u003e0.887758336\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.768115942028986%\\\"\\u003e\\n \\u003cp\\u003e1.475035441\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"8.333333333333334%\\\"\\u003e\\n \\u003cp\\u003e0.32\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n\\u003c/table\\u003e\"},{\"header\":\"4. Discussion\",\"content\":\"\\u003cp\\u003eIn this present study, we utilized GM data from a GWAS meta-analysis conducted by the MiBioGen Consortium and radiculopathy data published by the FinnGen Alliance R9. \\u0026nbsp;We aimed to revealthe causal relationship between gut microbiota and radiculopathy. \\u0026nbsp; The results identified eight gut microbiota that exhibited a causal relationship with radiculopathy. \\u0026nbsp; Furthermore, we conducted a reverse analysis to establish a unidirectional causal relationship, revealing no evidence that radiculopathy has an impact on the gut microbiota.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eAt present, the relationship between gut microbiota and orthopedic diseases is increasingly recognized. The impact of oral, gut, and skin microbiota on the causal relationship of the skeletal nervous system has been widely reported, leading to diseases such as disc herniation, lower back pain, and spinal deformities[8-9]. Short-Chain Fatty Acids (SCFAs) are a class of organic acids produced by the fermentation of dietary fiber in the gut by microbiota, with major SCFAs including propionic acid, acetic acid, and butyric acid. SCFAs are believed to reduce the occurrence of osteoporosis by influencing the proliferation and differentiation of osteoblasts and osteoclasts. In a mouse model of ovariectomy, SCFAs effectively reduce bone loss[10]. Disc degeneration is a significant factor leading to radiculopathy, and research has found 58 overlapping bacteria between intervertebral discs (IVD) and the gut, suggesting a connection between IVD and the gut[11]. Rajasekaran and others evaluated 24 lumbar IVDs and reported differences in the microbial composition between healthy discs and degenerative discs, indicating that gut microbiota may translocate through the intestinal epithelial barrier into intervertebral discs[12]. Wentian Li and colleagues found a blood-brain barrier-like function in intervertebral discs, hindering the immune system\\u0026apos;s surveillance of the disc\\u0026apos;s interior[10]. The lack of immune surveillance and hypoxic conditions in intervertebral discs provide ideal conditions for anaerobic bacteria to proliferate during invasion. Furthermore, damage to the intestinal epithelium can lead to bacteria and their toxic metabolites entering circulation, potentially triggering remote inflammation of intervertebral discs, resulting in disc degeneration. Compared to the typical gut microbiota, the composition of the gut microbiota in patients with chronic inflammatory pain shows significant changes in Actinobacteria, Firmicutes, and Bacteroidetes[13]. Considering the alteration of the microbiota as a potential therapeutic approach to maintaining or promoting skeletal nervous health is a rapidly developing research field. Our study elucidates the causal relationship between gut microbiota and radiculopathy, filling a gap in this field and laying the foundation for further understanding the mechanisms.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eRadiculopathy are mainly caused by chronic compression or infringement of nerve roots, with major contributing factors including ectopic ossification leading to bone spur formation, spinal canal stenosis, disc protrusion compression, osteoporosis, and local inflammation. Magnetic Resonance (MR) analysis of the gut microbiota and intervertebral disc degeneration shows that the presence of the Marvinbryantia microbial group is an important risk factor for intervertebral disc degeneration, consistent with our research results[3]. Currently, the influence of gut microbiota on osteoporosis and intervertebral disc lesions has been elucidated, which may be an indirect influencing factor for the occurrence of radiculopathy[14-15]. Experimental evidence suggests that SCFAs can inhibit the differentiation of primary bone marrow (BM) cells into osteoclasts, and butyrate salts can induce osteoblast differentiation and the formation of mineral nodules[16]. However, further basic experiments are needed to verify whether they can lead to heterotopic ossification. Gut microbiota plays a crucial role in the synthesis and absorption of vitamins, as they can synthesize B-group vitamins, vitamin K, and regulate the absorption of vitamins A, D, E, and K. Many studies have established a connection between vitamin K and B deficiencies and intervertebral disc degeneration. Additionally, deficiencies in vitamins B and D may affect nerve function[17-18]. It is speculated that gut microbiota may indirectly affect the development of radiculopathy through this pathway. Gut microbiota may influence radiculopathy through inflammation. Dietary tryptophan, a metabolite of Clostridium, has been found to directly act on astrocytes, limiting inflammation to alleviate neuropathic pain in mice[19]. Colonization of Bifidobacterium causes relapses in sterile mice with arthritis. In the experimental autoimmune encephalomyelitis (EAE) model, polysaccharide A (PSA) produced by Bacteroides fragilis promotes the development of regulatory T cells (Tregs) and provides protection against central nervous system inflammation and demyelination. Moreover, the gut microbiota can regulate chronic inflammatory pain through metabolites. Anaerobic bacteria such as Bacteroides, Bifidobacterium, and other bacteria produce short-chain fatty acids, which regulate T cell activation by binding to specific G protein-coupled receptors[20]. This process increases histone acetylation, ultimately regulating chronic inflammatory pain. Certain cell wall components, such as lipopolysaccharides (LPS), lipoteichoic acid (LTA), peptidoglycan (PGN), and \\u0026beta;-glucan, can enter the circulation and promote the development of chronic inflammatory pain[22,26]. Clarifying the molecular mechanisms by which the gut microbiota regulates pain may be a new drug target for the future treatment of peripheral nerve pain, including radiculopathy.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eNon-steroidal anti-inflammatory drugs and glucocorticoids are commonly used for the conservative treatment of radiculopathy[25]. Studies have found that intraperitoneal injection of dexamethasone in rats can regulate the gut microbiota, affecting the abundance of aerobic bacteria and lactobacilli, depending on the concentration. In a study of 155 people, ibuprofen was found to increase the abundance of Enterococcaceae, Enterobacteriaceae, Propionibacteriaceae, Pseudomonadaceae, Pseudonocardiaceae, and Salmonellaceae[23]. These studies suggest that painkillers commonly used for radiculopathy may affect the gut microbiota, and whether these effects in turn influence radiculopathy still requires further investigation. Solving orthopedic diseases from the perspective of gut microbiota is gaining increasing attention. In a clinical experiment in Germany, it was shown that a high-fiber diet effectively reduces bone absorption. Fecal transplantation and the use of probiotics protect bone health by inhibiting inflammatory reactions, regulating host immune responses, and maintaining gut microbiota, indirectly reducing the incidence of radiculopathy[21,24]. However, addressing nerve pain from the perspective of the gut microbiota is still in its early stages, and more experiments are needed to verify the relationship between the gut microbiota and radiculopathy.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eThis study represents the pioneering utilization of gut microbiome in Mendelian randomization analysis for investigating radiculopathy. \\u0026nbsp;A fundamental assumption of Mendelian randomization, which leverages an individual\\u0026apos;s genotype information to estimate causal effects, is that the genetic variation employed must exhibit a robust association with the exposure of interest while being independent from confounding factors. \\u0026nbsp; By satisfying these assumptions, Mendelian randomization can mitigate the impact of various uncontrolled confounders inherent in traditional observational studies and enhance the precision of inferring causality between gut microbiome and radiculopathy. \\u0026nbsp; Mendelian randomization has been extensively applied to elucidate the causal relationship between gut microbiota and skeletal nerve diseases such as disc degeneration, rheumatoid arthritis, and low back pain. \\u0026nbsp;Nevertheless, our study does have certain limitations. \\u0026nbsp;Firstly, due to concerns regarding population stratification bias, most participants included in our aggregated GWAS data were of European ancestry; \\u0026nbsp; however, radiculopathy predominantly occurs in East Asia. \\u0026nbsp;Consequently, it remains unclear whether intestinal microbes exert a causal influence on radiculopathy within Asian populations, potentially introducing some degree of error into our findings. \\u0026nbsp;Secondly, in order to incorporate more SNPs as instrumental variables (IVs), some SNPs utilized in our analysis did not meet the standard significance threshold (5*10-8), which may compromise result reproducibility\\u0026nbsp;\\u003c/p\\u003e\"},{\"header\":\"5. Conclusion\",\"content\":\"\\u003cp\\u003eUsing publicly available GWAS data, we performed a bidirectional, two-sample Mendelian randomization analysis of the causal relationship between gut microbiota and radiculopathy in 211 participants. The results of our analysis identified eight gut microbiota causally associated with radiculopathy. For more accurate results, more advanced MR Analysis and more aggregated GWAS data for gut microbiota and radiculopathy patients are needed.\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eFunding\\u003c/strong\\u003e: This study was supported by the National Natural Science Foundation of China (SHDC2020CR1024B)\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConflicts of interest/Competing interests\\u003c/strong\\u003e: None.\\u003c/p\\u003e\\u003ch2\\u003eAuthor Contribution\\u003c/h2\\u003e\\u003cp\\u003eJinyv Wang wrote the main manuscript text .Chen Yan and Linhui Han are responsible for data collection and analysisYiJuan Lu prepared figures 1-3. All authors reviewed the manuscript.\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\u003cli\\u003e\\u003cspan\\u003eNikolaidis I, Fouyas IP, Sandercock PA, Statham PF. Surgery for cervical radiculopathy or myelopathy. Cochrane Database Syst Rev. 2010;2010(1):CD001466. doi: \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.1002/14651858.CD001466.pub3\\u003c/span\\u003e\\u003cspan address=\\\"10.1002/14651858.CD001466.pub3\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e. PMID: 20091520; PMCID:PMC7084060.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eSu M, Tang Y, Kong W, Zhang S, Zhu T. Genetically supported causality between gut microbiota, gut metabolites and low back pain: a two-sample Mendelian randomization study. Front Microbiol. 2023;14:1157451. doi:\\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.3389/fmicb.2023.1157451\\u003c/span\\u003e\\u003cspan address=\\\"10.3389/fmicb.2023.1157451\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e. PMID: 37125171; PMCID: PMC10140346.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eGeng Z, Wang J, Chen G, Liu J, Lan J, Zhang Z, Miao J. Gut microbiota and intervertebral disc degeneration: a bidirectional two-sample Mendelian randomization study. J Orthop Surg Res. 2023;18(1):601. doi:\\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.1186/s13018-023-04081-0\\u003c/span\\u003e\\u003cspan address=\\\"10.1186/s13018-023-04081-0\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e. PMID: 37580794; PMCID: PMC10424333.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eLai B, Jiang H, Gao Y, Zhou X. Causal effects of gut microbiota on scoliosis:A bidirectional two-sample mendelian randomization study. 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Ther Adv Chronic Dis. 2022;13:20406223221091177. doi: \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.1177/20406223221091177\\u003c/span\\u003e\\u003cspan address=\\\"10.1177/20406223221091177\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e. PMID: 35924009; PMCID:PMC9340317.\\u003c/span\\u003e\\u003c/li\\u003e\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":true,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":false,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"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\":\"Gut microbiome, Mendelian randomization, radiculopathy,Intervertebral disc degeneration,pain\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-3863003/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-3863003/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003e\\u003cstrong\\u003eBackground\\u003c/strong\\u003ePrevious studies have suggested a potential link between the gut microbiome and radiculopathy, but the causal relationship remains unclear. Therefore, the aim of this study was to determine the causal effect of gut microbiome on radiculopathy using Mendelian randomization (MR) approach and single nucleotide polymorphisms (SNPs) associated with gut microbiome as instrumental variables\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eMethods \\u003c/strong\\u003eSummary data from genome-wide association studies of gut microbiota (the MiBioGen) and radiculopathy (the FinnGen biobank) were acquired. The inverse variance weighting (IVW) was chosen as the main MR Analysis method. The weighted median, MR-Egger regression, weighted model, and simple model were provided as additional supplements. Mendelian randomization pleiotropy residual sum and outlier (MR-PRESSO) and MR-Egger regression were performed to evaluate the horizontal pleiotropy and to eliminate outlier single-nucleotide polymorphisms (SNPs). Cochran’s Q-test was applied for heterogeneity detection.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eResults\\u003c/strong\\u003e We identified eight gut microbial taxa that were causally associated with radiculopathy (P\\u0026lt;0.05). The Cochrane Q test produced results that did not indicate heterogeneity (P\\u0026gt;0.05), indicating a lack of statistical significance. Furthermore, both the MR-Egger intercept test and the MR-PRESSO global test indicated that our findings were not influenced by horizontal pleiotropy (P\\u0026gt;0.05), further supporting the reliability of our results. In the reverse analysis, no evidence was found to suggest that radiculopathy has an impact on the gut microbiota.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConclusion\\u003c/strong\\u003eWe identified four gut microbiota that were protective against radiculopathy and four that may elevate the risk of the condition. Our findings confirmed a potential causal link between gut microbiota and radiculopathy, thereby providing a theoretical foundation for the development of targeted prevention strategies.\\u003c/p\\u003e\\n\\u003cp\\u003eKeywords\\u0026nbsp;\\u003c/p\\u003e\",\"manuscriptTitle\":\"Gut microbiota and radiculopathy: a bidirectional two-sample Mendelian randomization study\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2024-01-18 12:37:32\",\"doi\":\"10.21203/rs.3.rs-3863003/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"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\":\"98cbb8c4-f59f-4998-9a48-92b4cd23e2e2\",\"owner\":[],\"postedDate\":\"January 18th, 2024\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"posted\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2024-04-02T20:17:04+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2024-01-18 12:37:32\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-3863003\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-3863003\",\"identity\":\"rs-3863003\",\"version\":[\"v1\"]},\"buildId\":\"qtupq5eGEP_6zYnWcrvyt\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}