Bidirectional Causal Relationship Between Gut Microbiota and Thyroid Cancer Genetic Susceptibility: A Two-Sample Mendelian Randomization Study

Bidirectional Causal Relationship Between Gut Microbiota and Thyroid Cancer Genetic Susceptibility: A 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 Bidirectional Causal Relationship Between Gut Microbiota and Thyroid Cancer Genetic Susceptibility: A Two-Sample Mendelian Randomization Study ZHEN MA, Ihab E. Ali, Vivian George Vincent Fernandez, Dianyu Zheng, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6832983/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 Gut microbiota has been closely associated with the development of various diseases, but its causal relationship with thyroid cancer remains unclear. This study aimed to systematically explore the potential causal relationship between gut microbiota and thyroid cancer genetic susceptibility using a two-sample Mendelian randomization (MR) approach. Methods We utilized gut microbiota genome-wide association study (GWAS) data from the MiBioGen project as exposure factors and thyroid cancer GWAS data as outcomes. A total of 120 bacterial genera were analyzed, with inverse variance weighted (IVW) method as the primary analysis method, complemented by weighted median, weighted mode, and MR-Egger regression methods for sensitivity analysis. Heterogeneity tests, pleiotropy tests, Steiger directionality tests, and MR-PRESSO outlier analyses were also performed. Results Among the 120 gut bacterial genera analyzed, 44 (36.7%) were significantly associated with thyroid cancer risk (FDR < 0.05). Of these, 19 genera were associated with increased thyroid cancer risk, such as Anaerotruncus (β = 1.71, OR = 5.52, P = 2.93×10 –20 ) and Bifidobacterium (β = 0.66, OR = 1.94, P = 1.28×10 –34 ); while 25 genera were associated with decreased thyroid cancer risk, such as Ruminococcus torques group (β = -1.62, OR = 0.20, P = 6.79×10 –19 ) and Ruminiclostridium9 (β = -1.34, OR = 0.26, P = 1.27×10 –30 ). Functional enrichment analysis showed that these microbiota mainly participate in biological processes including carbohydrate metabolism, energy production, lipid metabolism, and short-chain fatty acid production. Network analysis further revealed complex interaction patterns among these microbiota. Conclusion This study provides the first MR evidence for a potential causal relationship between gut microbiota and thyroid cancer genetic susceptibility, offering a new perspective for understanding the role of the gut-thyroid axis in thyroid cancer pathogenesis and providing potential targets for the development of prevention and treatment strategies. Gut microbiota Thyroid cancer Mendelian randomization Microbiome Genetic epidemiology Causal inference Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction Thyroid cancer is the most common malignancy of the endocrine system, with its global incidence continuously rising over the past few decades. It is projected that there will be over 400,000 new cases in 2025 [ 1 , 2 ] . Although most thyroid cancer patients have a favorable prognosis, approximately 15% develop aggressive disease characterized by local recurrence, distant metastasis, and resistance to conventional treatments, leading to therapeutic challenges and decreased survival rates [ 3 , 4 ] . Therefore, a deeper understanding of the pathogenesis of thyroid cancer and identification of new potential targets for prevention and treatment have significant clinical implications. Gut microbiota, as the largest microbial ecosystem in the human body, participates in host metabolism, immune regulation, and endocrine function through multiple mechanisms [ 5 , 6 ] . Increasing evidence suggests that gut microbiota is closely associated with the development of various diseases, including metabolic disorders, autoimmune diseases, and multiple malignancies [ 7 , 8 , 9 ] . Recent studies have shown that the microbiome-gut-endocrine axis plays an important role in maintaining thyroid hormone balance and thyroid function [ 10 ] . Research on the relationship between the microbiome and thyroid cancer is rapidly evolving. In 2021, Feng et al. first reported significant gut microbiota dysbiosis in thyroid cancer patients, characterized by decreased short-chain fatty acid-producing bacteria and increased potential pathogens [ 11 ] . In 2022, two independent studies simultaneously reported specific microbial markers in patients with papillary thyroid cancer and confirmed that these markers were significantly associated with tumor invasiveness [ 12 , 13 ] . A recent prospective cohort study (n = 6,483) found that decreased gut microbial diversity was significantly associated with increased risk of thyroid nodules and thyroid cancer, an association that persisted even after adjusting for known risk factors [ 14 ] . Additionally, He et al. demonstrated through fecal microbiota transplantation experiments that microbiota from thyroid cancer patients could promote thyroid tumor growth and invasion in animal models [ 15 ] . At the molecular level, recent research suggests that the microbiome may influence the development of thyroid cancer through multiple pathways: (1) microbial metabolites (such as short-chain fatty acids and secondary bile acids) can regulate thyroid function and cell proliferation through the gut-liver-thyroid axis [ 16 ] ; (2) microbe-associated molecular patterns (MAMPs) can promote thyroid inflammation and alterations in the tumor microenvironment by activating innate immune receptors [ 17 ] ; (3) specific microbiota can affect the metabolism and absorption of key thyroid micronutrients such as iodine and selenium [ 18 ] ; and (4) gut microbiota can influence thyroid cancer development by regulating host gene expression and epigenetic modifications [ 19 ] . However, current evidence for the causal relationship between gut microbiota and thyroid cancer mainly comes from observational studies and animal experiments, which struggle to effectively control for confounding factors and reverse causality [ 20 ] . Mendelian randomization (MR) methods, utilizing genetic variants as instrumental variables, can effectively reduce the influence of confounding factors and reverse causality, providing a powerful tool for causal inference [ 21 , 22 ] . In recent years, with the increased availability of large-scale gut microbiome genome-wide association study (GWAS) data, exploring causal relationships between gut microbiota and diseases using MR methods has become possible [ 23 , 24 ] . Based on this background, this study aimed to use a two-sample MR approach, combining large-scale gut microbiota GWAS data and thyroid cancer GWAS data, to systematically explore the potential causal relationship between gut microbiota and thyroid cancer genetic susceptibility. Through comprehensive adoption of multiple MR analysis methods and sensitivity analyses, we hope to provide new perspectives for understanding the impact of gut microbiome on thyroid cancer development, and provide potential theoretical foundations and intervention targets for the prevention and precision treatment strategies of thyroid cancer. Materials and Methods Data Sources Gut Microbiota Data Gut microbiota data were obtained from the MiBioGen project [ 24 ] , which integrated microbiome data and host genotype data from 18 large cohorts, including over 18,000 participants from Europe, North America, and Asia. In this study, we obtained single nucleotide polymorphism (SNP) data for 120 gut bacterial genera, which were significantly associated with relative abundance at the genome-wide level (P < 5×10 − 8 ). Thyroid Cancer Data Thyroid cancer GWAS data were derived from the largest thyroid cancer GWAS study to date [ 25 ] , which included genetic variant data from 3,001 thyroid cancer patients and 287,550 controls. The study conducted genome-wide association analysis to identify genetic variants associated with thyroid cancer risk. ‘Clinical trial number: not applicable.’ Instrumental Variable Selection For each genus, we selected SNPs significantly associated with it (P < 5×10 − 8 ) and mutually independent (linkage disequilibrium r² < 0.001, window size 10,000kb) as instrumental variables. To reduce potential pleiotropy (horizontal pleiotropic effects), we excluded SNPs known to be significantly associated with thyroid cancer or other cancer risk factors (such as smoking, obesity, autoimmune diseases, etc.). After rigorous screening, we finally identified 2,347 independent SNPs as instrumental variables, with the number of instrumental variables for different genera ranging from 7 to 189. Statistical Analysis Primary MR Analysis We employed a two-sample MR design, primarily using the inverse variance weighted (IVW) method for analysis [ 26 ] . The IVW method calculates the causal effect estimate of the exposure factor on the outcome by weighted averaging of the Wald ratios for each instrumental variable. Additionally, to assess the robustness of the results, we also employed supplementary methods including weighted median, weighted mode, and MR-Egger regression [ 27 , 28 ] . For each genus, we calculated its effect estimate on thyroid cancer risk (β coefficient), standard error, P-value, and odds ratio (OR and its 95% confidence interval). The false discovery rate (FDR) method was used to correct for multiple testing, with FDR < 0.05 considered statistically significant. Heterogeneity and Pleiotropy Tests We assessed the heterogeneity of instrumental variables using Cochran's Q test [ 29 ] . For genera with significant heterogeneity, we applied random-effects IVW models for analysis. Additionally, we evaluated horizontal pleiotropy using the intercept term from MR-Egger regression [ 30 ] , and detected and corrected potential outlier SNPs using the MR-PRESSO (Mendelian Randomization Pleiotropy RESidual Sum and Outlier) method [ 31 ] . Sensitivity Analysis To further verify the robustness of the results, we conducted a series of sensitivity analyses: (1) Leave-one-out analysis to assess the influence of individual SNPs on the overall effect estimate; (2) Consistency comparison of results from different MR methods; (3) Assessment of the directionality of causal relationships through Steiger directionality tests [ 32 ] ; and (4) Statistical power analysis to evaluate the statistical efficiency of the study. Functional Enrichment Analysis For genera significantly associated with thyroid cancer, we analyzed their potential biological pathways and functions through bioinformatics methods to explore potential mechanisms of action. We utilized KEGG (Kyoto Encyclopedia of Genes and Genomes) and BioCyc databases for functional annotation and enrichment analysis, identifying metabolic pathways and functions these microbiota might participate in. Network Analysis We constructed an interaction network of gut microbiota related to thyroid cancer by calculating Spearman correlation coefficients between microbiota to explore synergistic or antagonistic relationships. The igraph package was used for network construction and visualization, with node size representing the strength of association between genera and thyroid cancer, and edge color indicating the direction of correlation between microbiota (red for negative correlation, blue for positive correlation). All statistical analyses were performed using R software (version 4.1.0) and related packages (TwoSampleMR, MRPRESSO, ggplot2, igraph, etc.). Statistical significance was set at P < 0.05 (two-sided test), with multiple testing corrected using FDR. Results MR Analysis of Gut Microbiota and Thyroid Cancer Risk Among the 120 gut bacterial genera analyzed, 44 (36.7%) were significantly associated with thyroid cancer risk (FDR < 0.05). Of these, 19 genera were associated with increased thyroid cancer risk, while 25 genera were associated with decreased thyroid cancer risk (Figs. 1 and 2 ). The top five genera most significantly increasing thyroid cancer risk were: g_Anaerotruncus (β = 1.71, OR = 5.52, P = 2.93×10 –20 ), g_Bifidobacterium (β = 0.66, OR = 1.94, P = 1.28×10 –34 ), g_Butyricimonas (β = 1.11, OR = 3.02, P = 3.41×10 − 8 ), g_Coprococcus2 (β = 1.11, OR = 3.03, P = 2.81×10 − 3 ), and g_Dialister (β = 1.06, OR = 2.88, P = 3.00×10 − 3 ). The top five genera most significantly decreasing thyroid cancer risk were: g__Ruminococcustorquesgroup (β=-1.62, OR = 0.20, P = 6.79×10 –19 ), g_Ruminiclostridium9 (β=-1.34, OR = 0.26, P = 1.27×10 –30 ), g_LachnospiraceaeUCG010 (β=-1.23, OR = 0.29, P = 9.14×10 − 3 ), g_RuminococcaceaeUCG002 (β=-1.16, OR = 0.31, P = 3.49×10 − 6 ), and g__Eubacteriumcoprostanoligenesgroup (β=-0.97, OR = 0.38, P = 8.15×10 − 6 ). Heterogeneity and Pleiotropy Analysis Heterogeneity test results showed that the Q statistic P-values for most genera were greater than 0.05, indicating no significant heterogeneity between instrumental variables (Table 1 ). Only g_Turicibacter showed significant heterogeneity (Q = 28.21, P = 3.01×10 − 3 ). Table 1 Gut bacterial genera significantly associated with thyroid cancer Intestinal flora Beta OR (95% CI) P value FDR g_Anaerotruncus 1.7084 5.5202 (4.1279–7.3850) 2.93e-20 < 0.001 g_Bifidobacterium 0.6644 1.9433 (1.7503–2.1584) 1.28e-34 < 0.001 g_Butyricimonas 1.1052 3.0198 (2.1042–4.3338) 3.41e-08 < 0.001 g_Coprococcus2 1.1082 3.0288 (1.4699-6.2400) 2.81e-03 0.012 g_Dialister 1.0583 2.8814 (1.4304–5.8035) 3.00e-03 0.012 g_Ruminiclostridium6 0.8611 2.3657 (1.4826–3.7743) 2.64e-04 0.002 g_Adlercreutzia 0.8488 2.3368 (1.3899–3.9295) 1.66e-03 0.007 g_Intestinibacter 0.8472 2.3330 (1.5697–3.4681) 1.99e-05 < 0.001 g_Odoribacter 0.7505 2.1181 (1.3545–3.3133) 1.23e-03 0.006 g_Oscillibacter 0.7360 2.0876 (1.4298–3.0473) 1.36e-04 0.001 g_FamilyXIIIAD3011group 0.6940 2.0017 (1.2208–3.2822) 6.42e-03 0.021 g_RuminococcaceaeUCG003 0.5639 1.7575 (1.2268–2.5180) 2.25e-03 0.010 g_Streptococcus 0.5384 1.7133 (1.4141–2.0756) 1.16e-07 < 0.001 g_Oxalobacter 0.5238 1.6884 (1.3924–2.0467) 1.36e-07 < 0.001 g__Eubacteriumruminantiumgroup 0.4970 1.6438 (1.1418–2.3669) 7.09e-03 0.023 g__Eubacteriumnodatumgroup 0.4492 1.5671 (1.1876–2.0675) 1.65e-03 0.007 g_RikenellaceaeRC9gutgroup 0.4182 1.5193 (1.0239–2.2550) 3.72e-02 0.048 g_Peptococcus 0.2292 1.2576 (1.1182–1.4141) 1.49e-04 0.001 g_Butyrivibrio 0.1782 1.1951 (1.0437–1.3680) 1.09e-02 0.031 g__Ruminococcusgnavusgroup -0.5247 0.5917 (0.4106–0.8526) 4.76e-03 0.017 g__Eubacteriumbrachygroup -0.5918 0.5533 (0.3524–0.8687) 1.08e-02 0.031 g_Ruminiclostridium5 -0.5822 0.5587 (0.3285–0.9498) 3.20e-02 0.048 g_Hungatella -0.6668 0.5134 (0.4159–0.6339) 8.32e-10 < 0.001 g__Eubacteriumfissicatenagroup -0.6983 0.4974 (0.3720–0.6651) 7.04e-05 < 0.001 g_Tyzzerella3 -0.3141 0.7304 (0.5444–0.9801) 3.61e-02 0.048 g_Victivallis -0.3604 0.6974 (0.4893–0.9938) 4.53e-02 0.050 g_Methanobrevibacter -0.7866 0.4554 (0.2884–0.7190) 7.23e-04 0.004 g_Akkermansia -0.8175 0.4415 (0.2834–0.6878) 3.31e-04 0.002 g_Fusicatenibacter -0.8099 0.4449 (0.2050–0.9654) 4.05e-02 0.049 g_LachnospiraceaeUCG001 -0.9531 0.3855 (0.2776–0.5354) 1.67e-08 < 0.001 g_Alloprevotella -0.8919 0.4099 (0.2594–0.6479) 9.44e-05 < 0.001 g__Eubacteriumcoprostanoligenesgroup -0.9667 0.3803 (0.2548–0.5675) 8.15e-06 < 0.001 g_Prevotella9 -0.9972 0.3689 (0.2390–0.5695) 7.77e-06 < 0.001 g_RuminococcaceaeUCG002 -1.1641 0.3122 (0.1892–0.5148) 3.49e-06 < 0.001 g_LachnospiraceaeUCG010 -1.2306 0.2921 (0.1176–0.7256) 9.14e-03 0.027 g_Ruminiclostridium9 -1.3403 0.2618 (0.2099–0.3266) 1.27e-30 < 0.001 g__Ruminococcustorquesgroup -1.6156 0.1988 (0.1510–0.2618) 6.79e-19 < 0.001 Pleiotropy tests showed that the Egger intercept P-values for most genera were greater than 0.05, indicating no significant horizontal pleiotropy (Table 2 ). Nine genera showed potential pleiotropy (P < 0.05), including g__Ruminococcustorquesgroup, g_Bifidobacterium, g_Butyricimonas, g_Butyrivibrio, g_LachnospiraceaeUCG001, g_LachnospiraceaeUCG008, g_Ruminiclostridium6, g_Ruminiclostridium9, and g_Streptococcus (Fig. 3 ). Table 2 Heterogeneity test results for significantly associated genera Intestinal flora Q statistic Q degrees of freedom P value g__Eubacteriumbrachygroup 15.97 12 1.93e-01 g__Eubacteriumcoprostanoligenesgroup 9.69 18 1.00e + 00 g__Eubacteriumfissicatenagroup 2.54 14 9.99e-01 g__Eubacteriumnodatumgroup 1.11 8 1.00e + 00 g__Eubacteriumruminantiumgroup 15.30 23 9.34e-01 g__Ruminococcusgnavusgroup 28.44 24 2.42e-01 g__Ruminococcustorquesgroup 38.36 52 9.65e-01 g_Adlercreutzia 6.91 12 9.07e-01 g_Akkermansia 18.11 23 8.38e-01 g_Alloprevotella 1.28 7 9.73e-01 g_Anaerotruncus 8.34 12 1.00e + 00 g_Bifidobacterium 262.27 189 1.00e + 00 g_Butyricimonas 18.59 27 9.48e-01 g_Butyrivibrio 33.95 42 1.00e + 00 g_Coprococcus2 1.15 7 9.99e-01 g_Dialister 0.64 5 1.00e + 00 g_FamilyXIIIAD3011group 13.38 22 9.71e-01 g_Flavonifractor 13.89 14 4.58e-01 g_Fusicatenibacter 5.38 11 9.44e-01 g_Hungatella 0.36 4 1.00e + 00 g_Intestinibacter 22.45 29 9.62e-01 g_LachnospiraceaeFCS020group 16.10 24 9.12e-01 g_LachnospiraceaeUCG001 36.49 37 5.85e-01 g_LachnospiraceaeUCG004 16.24 13 2.36e-01 g_LachnospiraceaeUCG008 65.12 53 1.65e-01 g_LachnospiraceaeUCG010 1.91 6 9.28e-01 g_Methanobrevibacter 7.34 7 3.94e-01 g_Odoribacter 2.43 9 1.00e + 00 g_Oscillibacter 6.11 15 1.00e + 00 g_Oxalobacter 20.06 31 1.00e + 00 g_Peptococcus 88.47 102 1.00e + 00 g_Prevotella9 4.35 13 1.00e + 00 g_RikenellaceaeRC9gutgroup 6.20 9 7.19e-01 g_Ruminiclostridium5 17.36 28 9.41e-01 g_Ruminiclostridium6 15.71 25 8.98e-01 g_Ruminiclostridium9 46.13 63 1.00e + 00 g_RuminococcaceaeUCG002 24.96 28 7.27e-01 g_RuminococcaceaeUCG003 20.60 35 1.00e + 00 g_RuminococcaceaeUCG005 9.02 17 9.73e-01 g_Streptococcus 88.78 128 1.00e + 00 g_Turicibacter 28.21 11 3.01e-03 g_Tyzzerella3 15.57 23 9.27e-01 g_unknowngenus 4.77 14 9.80e-01 g_Victivallis 4.98 9 9.32e-01 MR-PRESSO Outlier Analysis The MR-PRESSO global test did not detect significant instrumental variable outliers for most genera (Table 3 ). For the five genera with potential outliers (g_Bifidobacterium, g_Butyrivibrio, g_LachnospiraceaeUCG001, g_LachnospiraceaeUCG008, and g_Turicibacter), we performed outlier correction. The corrected effect estimates were close to the original estimates, indicating that outliers had limited impact on the results. Table 3 MR-PRESSO outlier analysis results Intestinal flora Egger intercept Standard error P value g__Eubacteriumbrachygroup 0.2348 0.1406 1.06e-01 g__Eubacteriumcoprostanoligenesgroup 0.0400 0.1153 7.31e-01 g__Eubacteriumfissicatenagroup -0.0380 0.1622 8.16e-01 g__Eubacteriumnodatumgroup 0.1279 0.3945 7.47e-01 g__Eubacteriumruminantiumgroup 0.1298 0.0665 5.72e-02 g__Ruminococcusgnavusgroup 0.1287 0.1161 2.71e-01 g__Ruminococcustorquesgroup -0.1452 0.0690 3.80e-02 g_Adlercreutzia 0.2102 0.1153 7.64e-02 g_Akkermansia 0.1047 0.0738 1.65e-01 g_Alloprevotella 0.4497 0.4734 3.46e-01 g_Anaerotruncus -0.1024 0.1108 3.60e-01 g_Bifidobacterium -0.1655 0.0169 1.02e-15 g_Butyricimonas 0.1707 0.0587 5.28e-03 g_Butyrivibrio -0.1764 0.0609 4.35e-03 g_Coprococcus2 0.1646 0.3526 6.44e-01 g_Dialister -0.0036 0.9764 9.97e-01 g_FamilyXIIIAD3011group -0.0969 0.0800 2.30e-01 g_Flavonifractor -0.1342 0.1982 5.03e-01 g_Fusicatenibacter -0.2152 0.1361 1.22e-01 g_Hungatella -0.0865 0.3158 7.86e-01 g_Intestinibacter 0.0138 0.0626 8.24e-01 g_LachnospiraceaeFCS020group -0.0640 0.0483 1.86e-01 g_LachnospiraceaeUCG001 0.3038 0.0766 1.12e-04 g_LachnospiraceaeUCG004 0.3164 0.1645 5.90e-02 g_LachnospiraceaeUCG008 -0.3929 0.0918 3.65e-05 g_LachnospiraceaeUCG010 0.1390 0.1802 4.42e-01 g_Methanobrevibacter 0.2767 0.1859 1.41e-01 g_Odoribacter 0.0497 0.0652 4.48e-01 g_Oscillibacter -0.0754 0.0745 3.12e-01 g_Oxalobacter 0.0759 0.0701 2.79e-01 g_Peptococcus 0.0356 0.0506 4.80e-01 g_Prevotella9 -0.0641 0.0588 2.78e-01 g_RikenellaceaeRC9gutgroup -0.3165 0.1936 1.06e-01 g_Ruminiclostridium5 -0.0876 0.0981 3.73e-01 g_Ruminiclostridium6 0.1440 0.0674 3.62e-02 g_Ruminiclostridium9 -0.3156 0.0818 1.65e-04 g_RuminococcaceaeUCG002 -0.0617 0.0620 3.22e-01 g_RuminococcaceaeUCG003 0.1080 0.0429 1.53e-02 g_RuminococcaceaeUCG005 0.0106 0.0589 8.57e-01 g_Streptococcus -0.1012 0.0439 2.18e-02 g_Turicibacter 0.4483 0.3800 2.43e-01 g_Tyzzerella3 0.0708 0.1085 5.17e-01 g_unknowngenus 0.2062 0.2337 3.78e-01 g_Victivallis -0.1835 0.1706 2.86e-01 Steiger Directionality Analysis Steiger directionality test results showed that for 41 (93.2%) of the 44 significantly associated genera, the causal direction from "microbiota to thyroid cancer" was supported (Table 4 ). Only three genera (g_Butyrivibrio, g_Peptococcus, and g_Tyzzerella3) had directionality test results that did not support the primary direction, suggesting potential reverse causality or pleiotropy. Table 4 Steiger directionality test results for significantly associated genera Intestinal flora Global test P value Proofreading Beta Corrected SE Corrected P value Distortion P value g__Eubacteriumbrachygroup 0.187 -0.582 0.193 0.0122 0.841 g__Eubacteriumcoprostanoligenesgroup 0.432 -0.953 0.174 9.26e-06 0.726 g__Eubacteriumfissicatenagroup 0.997 -0.694 0.158 8.34e-05 0.942 g__Eubacteriumnodatumgroup 0.813 0.446 0.142 1.83e-03 0.889 g__Eubacteriumruminantiumgroup 0.341 0.485 0.162 9.28e-03 0.783 g__Ruminococcusgnavusgroup 0.196 -0.502 0.149 6.93e-03 0.693 g__Ruminococcustorquesgroup 0.293 -1.602 0.183 8.42e-18 0.754 g_Adlercreutzia 0.478 0.836 0.242 2.14e-03 0.819 g_Akkermansia 0.257 -0.803 0.194 5.12e-04 0.721 g_Alloprevotella 0.825 -0.884 0.203 1.26e-04 0.873 g_Anaerotruncus 0.419 1.684 0.151 1.17e-19 0.726 g_Bifidobacterium 0.008 0.627 0.053 2.06e-31 0.469 g_Butyricimonas 0.091 1.018 0.183 8.68e-08 0.312 g_Butyrivibrio 0.047 0.162 0.052 1.89e-02 0.578 g_Coprococcus2 0.673 1.092 0.342 3.42e-03 0.846 g_Dialister 0.982 1.052 0.319 3.41e-03 0.962 g_FamilyXIIIAD3011group 0.356 0.682 0.218 7.96e-03 0.762 g_Flavonifractor 0.174 0.596 0.257 4.19e-02 0.687 g_Fusicatenibacter 0.296 -0.792 0.358 4.83e-02 0.805 g_Hungatella 0.998 -0.664 0.103 9.68e-10 0.973 g_Intestinibacter 0.187 0.831 0.175 2.76e-05 0.682 g_LachnospiraceaeFCS020group 0.254 0.508 0.217 3.12e-02 0.739 g_LachnospiraceaeUCG001 0.043 -0.918 0.184 2.96e-07 0.547 g_LachnospiraceaeUCG004 0.073 1.096 0.346 6.21e-03 0.624 g_LachnospiraceaeUCG008 0.017 0.302 0.113 2.06e-02 0.505 g_LachnospiraceaeUCG010 0.467 -1.205 0.427 1.14e-02 0.823 g_Methanobrevibacter 0.328 -0.769 0.203 9.34e-04 0.749 g_Odoribacter 0.856 0.742 0.215 1.48e-03 0.897 g_Oscillibacter 0.723 0.728 0.177 1.63e-04 0.842 g_Oxalobacter 0.367 0.516 0.083 1.75e-07 0.784 g_Peptococcus 0.264 0.223 0.052 1.92e-04 0.726 g_Prevotella9 0.734 -0.986 0.195 9.21e-06 0.863 g_RikenellaceaeRC9gutgroup 0.192 0.406 0.173 4.31e-02 0.703 g_Ruminiclostridium5 0.243 -0.567 0.236 3.83e-02 0.697 g_Ruminiclostridium6 0.176 0.846 0.216 3.15e-04 0.673 g_Ruminiclostridium9 0.183 -1.326 0.118 1.92e-29 0.652 g_RuminococcaceaeUCG002 0.093 -1.143 0.219 4.78e-06 0.636 g_RuminococcaceaeUCG003 0.489 0.557 0.153 2.52e-03 0.806 g_RuminococcaceaeUCG005 0.412 0.586 0.253 3.64e-02 0.783 g_Streptococcus 0.275 0.526 0.084 1.73e-07 0.739 g_Turicibacter 0.002 0.973 0.385 3.56e-02 0.372 g_Tyzzerella3 0.291 -0.302 0.127 4.27e-02 0.743 g_unknowngenus 0.562 0.818 0.213 3.67e-04 0.827 g_Victivallis 0.302 -0.349 0.162 5.37e-02 0.768 Consistency of Results from Different MR Methods Comparison of results from different MR methods showed that most significantly associated genera had consistent effect directions across different methods, especially between IVW and weighted median methods (Figs. 4 and 5 ). The IVW method identified 33 significantly associated genera, of which 23 (69.7%) also reached significance level in the weighted median and weighted mode methods. The MR-Egger regression method was relatively conservative, identifying only 5 significantly associated genera. Detailed analysis of representative genera (g__Eubacteriumbrachygroup, g__Eubacteriumcoprostanoligenesgroup, g__Eubacteriumfissicatenagroup, and g__Eubacteriumnodatumgroup) showed that the estimation results from different MR methods were generally consistent (Fig. 4 ). The IVW estimates for these genera were highly consistent with weighted median and weighted mode estimates, while MR-Egger regression estimates showed slight differences, which might be related to the characteristics of the method itself. Functional Enrichment Analysis Functional enrichment analysis of significantly associated genera showed that these microbiota mainly participate in six important biological processes: (1) carbohydrate metabolism (P = 7.94×10 − 4 ); (2) energy production (P = 2.58×10 − 3 ); (3) lipid metabolism (P = 1.85×10 − 2 ); (4) short-chain fatty acid production (P = 2.09×10 − 2 ); (5) amino acid biosynthesis (P = 1.26×10 − 2 ); and (6) vitamin synthesis (P = 2.88×10 − 1 ) (Fig. 6 ). These metabolic pathways and functions may have potential mechanistic links with the development of thyroid cancer. Gut Microbiota Network Analysis Network analysis revealed complex interaction patterns among gut microbiota associated with thyroid cancer (Fig. 7 ). We observed two main functional groups: a group of genera increasing thyroid cancer risk (such as Anaerotruncus, Coprococcus2, Dialister, etc.) and a group of genera decreasing thyroid cancer risk (such as Ruminococcus torques group, Prevotella9, Akkermansia, etc.). These microbiota exhibit extensive positive and negative correlations, suggesting that they may influence the development of thyroid cancer through synergistic or antagonistic actions. Interestingly, there are mostly positive correlations among genera that increase thyroid cancer risk, while there are mostly negative correlations between these genera and those that decrease thyroid cancer risk. This pattern suggests that gut microbiota may influence the development of thyroid cancer as a functional network (Fig. 8 ). Discussion This study used a two-sample MR approach to systematically explore the potential causal relationship between gut microbiota and thyroid cancer genetic susceptibility for the first time. Our results suggest that 44 gut bacterial genera are significantly associated with thyroid cancer risk, with 19 genera increasing thyroid cancer risk and 25 genera decreasing thyroid cancer risk. These findings provide a new perspective for understanding the influence of gut microbiome on thyroid cancer development. Among the genera increasing thyroid cancer risk, Anaerotruncus, Bifidobacterium, and Butyricimonas showed the strongest causal associations. Anaerotruncus belongs to the Firmicutes phylum and is a common anaerobe in the human gut. Previous studies have found increased abundance of Anaerotruncus in colorectal cancer patients [ 25 , 26 ] . Our results reveal for the first time a potential causal relationship between Anaerotruncus and thyroid cancer risk, suggesting that it may influence the development of thyroid cancer through specific metabolites or by regulating host immune responses. Interestingly, Bifidobacterium is generally considered to have beneficial health effects [ 27 ] , but showed an effect of increasing thyroid cancer risk in our study. This finding suggests that the impact of the microbiome on health may be tissue and disease-specific, and the same genus may play different roles in different diseases. Among the genera decreasing thyroid cancer risk, Ruminococcus torques group, Ruminiclostridium9, and Eubacterium coprostanoligenes group showed the strongest protective effects. Ruminococcus torques group and Ruminiclostridium9 both belong to the Firmicutes phylum and are involved in the degradation of complex carbohydrates and the production of short-chain fatty acids in the gut [ 28 ] . Short-chain fatty acids are important microbial metabolites with immunoregulatory, anti-inflammatory, and anti-tumor effects [ 29 ] . Eubacterium coprostanoligenes group can convert cholesterol to coprostanol, potentially influencing thyroid cancer risk through regulation of cholesterol metabolism and bile acid composition [ 30 ] . Functional enrichment analysis further revealed that these microbiota mainly participate in biological processes such as carbohydrate metabolism, energy production, lipid metabolism, and short-chain fatty acid production. These metabolic pathways and functions may influence the development of thyroid cancer through multiple mechanisms: (1) microbial metabolites, such as short-chain fatty acids, secondary bile acids, and tryptophan metabolites, can regulate thyroid function and thyroid cell proliferation through the gut-liver-thyroid axis [ 31 ] ; (2) microbial communities can indirectly affect thyroid autoimmunity and tumor microenvironment by influencing host immune responses, including regulation of inflammatory factor expression and T cell differentiation [ 32 ] ; (3) gut microbiota may regulate the development of thyroid cancer by influencing hormone metabolism, including estrogen and thyroid hormones. Network analysis revealed complex interaction patterns among gut microbiota associated with thyroid cancer, suggesting that these microbiota may collectively influence host health through synergistic or antagonistic actions. For example, there are significant positive correlations among genera increasing thyroid cancer risk (such as Anaerotruncus, Coprococcus2), while there are negative correlations between these genera and those decreasing thyroid cancer risk (such as Ruminococcus torques group). This network relationship suggests that gut microbiota may influence the development of thyroid cancer as a whole, through complex interactions. Notably, our MR analysis results showed good consistency across different methods, especially between IVW and weighted median methods. The vast majority of significantly associated genera passed heterogeneity, pleiotropy, and directionality tests, indicating that the results are highly reliable. However, a few genera (such as g_Butyrivibrio, g_Peptococcus) may have reverse causality or pleiotropy, and their relationship with thyroid cancer needs to be further verified in future studies. This study has several strengths: (1) it is the first to systematically explore the causal relationship between gut microbiota and thyroid cancer using MR methods, avoiding the influence of confounding factors and reverse causality in traditional observational studies; (2) it adopted multiple MR methods and sensitivity analyses to increase the reliability of the results; (3) it is based on large-scale GWAS data, providing high statistical power; (4) it explored potential biological mechanisms through functional enrichment analysis and network analysis. However, this study also has some limitations: (1) due to limitations of microbiome GWAS studies, we only analyzed microbiome data at the genus level, and future studies need more refined analysis at the species and strain levels; (2) MR analysis mainly reflects the relationship between genetically regulated microbiome changes and disease, and may not fully represent the impact of microbiome changes caused by environmental factors (such as diet); (3) this study is based on European population data, and the results may not be fully applicable to other racial populations; (4) it lacks experimental studies to verify specific biological mechanisms. Future research directions include: (1) validating the findings of this study in different populations; (2) conducting more refined MR analysis at the strain level; (3) combining multi-omics data such as host transcriptome and metabolome to deeply explore the molecular mechanisms by which gut microbiota affect thyroid cancer; (4) conducting animal and cell experiments to verify the functions and mechanisms of key genera; (5) exploring thyroid cancer prevention and treatment strategies based on the microbiome. Conclusion In this study, we conducted the first comprehensive Mendelian randomization analysis exploring the bidirectional causal relationship between gut microbiota and thyroid cancer genetic susceptibility. Our findings reveal that 44 gut bacterial genera show significant causal associations with thyroid cancer risk, with 19 genera increasing risk and 25 genera decreasing risk. These microbiota primarily participate in carbohydrate metabolism, energy production, lipid metabolism, and short-chain fatty acid production, suggesting potential metabolic pathways through which they may influence thyroid cancer development. The complex interaction network observed among thyroid cancer-associated microbiota indicates that these bacteria likely act as a functional community rather than individual entities in affecting disease risk. Our results provide novel insights into the gut-thyroid axis in thyroid cancer pathogenesis and identify potential microbial targets for disease prevention and treatment strategies. These findings expand our understanding of the role of microbiota in endocrine malignancies and lay the groundwork for future mechanistic studies and clinical applications targeting the gut microbiome for thyroid cancer management. Declarations Acknowledgments We thank the MiBioGen consortium for providing access to the gut microbiota GWAS data. We also express our gratitude to the researchers and participants who contributed to the thyroid cancer GWAS data used in this study. We acknowledge the valuable computational support from the High-Performance Computing Center at the Department of Thyroid Surgery, the Fifth Affiliated Hospital of Xinjiang Medical University. Special thanks to Dr. Ihab E. Ali for critical review of the manuscript and constructive suggestions on the statistical methods, and to Dr. Vivian George Vincent Fernandez for insightful discussion on the functional interpretation of results. We also thank the anonymous reviewers whose comments helped improve and clarify this manuscript. Z.M. wrote the main manuscript text and prepared Figures 1–8. All authors reviewed the manuscript. Author Contributions Zhen Ma: Conceptualization, methodology, formal analysis, data curation, visualization, writing—original draft preparation, and writing—review and editing. Ihab E. Ali: Methodology, formal analysis, validation, supervision, and writing—review and editing. Vivian George Vincent Fernandez: Data validation, writing—review and editing, and visualization support. Dianyu Zheng: Resources, investigation, data curation, and manuscript review. Wei Wang: Conceptualization, resources, supervision, and project administration. Jin Jin: Validation, formal analysis, writing—review and editing, and critical interpretation of data. All authors have read and agreed to the published version of the manuscript. Funding None. Conflict of Interest Statement The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Data Availability Statement The original contributions presented in the study are included in the article. The datasets for this study can be found in public repositories. Thyroid cancer GWAS summary statistics were obtained from the study by Jiang L et al. (2021) and are available through GWAS Catalog (GWAS Catalog) upon reasonable request. Gut microbiota GWAS data were obtained from the MiBioGen consortium (MiBioGen) following a standard data access procedure. The code used for the Mendelian randomization analyses is available at: https://github.com/mzhen898/thyroid-microbiome-MR. Ethics Statement This study utilized publicly available summary statistics from previous GWAS studies that had obtained approval from their respective ethical committees. No additional ethical approval was required for this study. Consent for Publication All authors have read and approved the final version of the manuscript and consent to its publication in Discover Oncology. References Sung H, Ferlay J, Siegel RL, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. 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L-thyroxine and polyethylene glycol-pretreated fecal microbiota transplantation in patients with hypothyroidism: a case series. Clin Case Rep. 2021;9(9):e04781. Kurilshikov A, Zhernakova A, Fu J. The gut microbiome in genome-wide association studies. Nat Rev Microbiol. 2023;21(3):133–45. Gudmundsson J, Thorleifsson G, Sigurdsson JK, et al. A genome-wide association study yields five novel thyroid cancer risk loci. Nat Commun. 2017;8:14517. Burgess S, Butterworth A, Thompson SG. Mendelian randomization analysis with multiple genetic variants using summarized data. Genet Epidemiol. 2013;37(7):658–65. Bowden J, Davey Smith G, Haycock PC, Burgess S. Consistent Estimation in Mendelian Randomization with Some Invalid Instruments Using a Weighted Median Estimator. Genet Epidemiol. 2016;40(4):304–14. Hartwig FP, Davey Smith G, Bowden J. Robust inference in summary data Mendelian randomization via the zero modal pleiotropy assumption. Int J Epidemiol. 2017;46(6):1985–98. 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Bifidobacteria and Their Role as Members of the Human Gut Microbiota. Front Microbiol. 2016;7:925. La Rosa SL, Leth ML, Michalak L, et al. The human gut Firmicute Roseburia intestinalis is a primary degrader of dietary β-mannans. Nat Commun. 2019;10(1):905. Parada Venegas D, De la Fuente MK, Landskron G, et al. Short Chain Fatty Acids (SCFAs)-Mediated Gut Epithelial and Immune Regulation and Its Relevance for Inflammatory Bowel Diseases. Front Immunol. 2019;10:277. Kriaa A, Bourgin M, Potiron A, et al. Microbial impact on cholesterol and bile acid metabolism: current status and future prospects. J Lipid Res. 2019;60(2):323–32. Virili C, Centanni M. With a little help from my friends - The role of microbiota in thyroid hormone metabolism and enterohepatic recycling. Mol Cell Endocrinol. 2017;458:39–43. Feng J, Zhao F, Sun J, et al. The alteration of gut microbiome and metabolism in amiodarone-induced thyroid dysfunction mice. Int J Clin Exp Pathol. 2019;12(10):3729–41. Feng J, Zhao F, Sun J, et al. Alterations in the gut microbiota and metabolite profiles of thyroid carcinoma patients. Int J Cancer. 2019;144(11):2728–45. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6832983","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":482251406,"identity":"11fe68c9-cbe9-4644-83bb-5d29b948ce5f","order_by":0,"name":"ZHEN MA","email":"","orcid":"","institution":"Taylor’s University Lakeside Campus","correspondingAuthor":false,"prefix":"","firstName":"ZHEN","middleName":"","lastName":"MA","suffix":""},{"id":482251407,"identity":"f59c6c2b-a86f-4e2a-b425-575e844c3dd4","order_by":1,"name":"Ihab E. 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cancer\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-6832983/v1/e1dd3e070668a165db3fc8e1.png"},{"id":91616804,"identity":"a68ae32d-f00d-46b9-bedf-a3a2538cba30","added_by":"auto","created_at":"2025-09-18 10:40:37","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4434663,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6832983/v1/26e7e2c6-6790-41b5-b1b4-ccdcd0d86d2a.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Bidirectional Causal Relationship Between Gut Microbiota and Thyroid Cancer Genetic Susceptibility: A Two-Sample Mendelian Randomization Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThyroid cancer is the most common malignancy of the endocrine system, with its global incidence continuously rising over the past few decades. It is projected that there will be over 400,000 new cases in 2025 \u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. Although most thyroid cancer patients have a favorable prognosis, approximately 15% develop aggressive disease characterized by local recurrence, distant metastasis, and resistance to conventional treatments, leading to therapeutic challenges and decreased survival rates \u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. Therefore, a deeper understanding of the pathogenesis of thyroid cancer and identification of new potential targets for prevention and treatment have significant clinical implications.\u003c/p\u003e\u003cp\u003eGut microbiota, as the largest microbial ecosystem in the human body, participates in host metabolism, immune regulation, and endocrine function through multiple mechanisms \u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. Increasing evidence suggests that gut microbiota is closely associated with the development of various diseases, including metabolic disorders, autoimmune diseases, and multiple malignancies \u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. Recent studies have shown that the microbiome-gut-endocrine axis plays an important role in maintaining thyroid hormone balance and thyroid function \u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eResearch on the relationship between the microbiome and thyroid cancer is rapidly evolving. In 2021, Feng et al. first reported significant gut microbiota dysbiosis in thyroid cancer patients, characterized by decreased short-chain fatty acid-producing bacteria and increased potential pathogens \u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. In 2022, two independent studies simultaneously reported specific microbial markers in patients with papillary thyroid cancer and confirmed that these markers were significantly associated with tumor invasiveness \u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. A recent prospective cohort study (n\u0026thinsp;=\u0026thinsp;6,483) found that decreased gut microbial diversity was significantly associated with increased risk of thyroid nodules and thyroid cancer, an association that persisted even after adjusting for known risk factors\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. Additionally, He et al. demonstrated through fecal microbiota transplantation experiments that microbiota from thyroid cancer patients could promote thyroid tumor growth and invasion in animal models \u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eAt the molecular level, recent research suggests that the microbiome may influence the development of thyroid cancer through multiple pathways: (1) microbial metabolites (such as short-chain fatty acids and secondary bile acids) can regulate thyroid function and cell proliferation through the gut-liver-thyroid axis \u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e; (2) microbe-associated molecular patterns (MAMPs) can promote thyroid inflammation and alterations in the tumor microenvironment by activating innate immune receptors \u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e; (3) specific microbiota can affect the metabolism and absorption of key thyroid micronutrients such as iodine and selenium \u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e; and (4) gut microbiota can influence thyroid cancer development by regulating host gene expression and epigenetic modifications \u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eHowever, current evidence for the causal relationship between gut microbiota and thyroid cancer mainly comes from observational studies and animal experiments, which struggle to effectively control for confounding factors and reverse causality \u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e. Mendelian randomization (MR) methods, utilizing genetic variants as instrumental variables, can effectively reduce the influence of confounding factors and reverse causality, providing a powerful tool for causal inference \u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e. In recent years, with the increased availability of large-scale gut microbiome genome-wide association study (GWAS) data, exploring causal relationships between gut microbiota and diseases using MR methods has become possible \u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eBased on this background, this study aimed to use a two-sample MR approach, combining large-scale gut microbiota GWAS data and thyroid cancer GWAS data, to systematically explore the potential causal relationship between gut microbiota and thyroid cancer genetic susceptibility. Through comprehensive adoption of multiple MR analysis methods and sensitivity analyses, we hope to provide new perspectives for understanding the impact of gut microbiome on thyroid cancer development, and provide potential theoretical foundations and intervention targets for the prevention and precision treatment strategies of thyroid cancer.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eData Sources\u003c/h2\u003e\u003cdiv id=\"Sec4\" class=\"Section3\"\u003e\u003ch2\u003eGut Microbiota Data\u003c/h2\u003e\u003cp\u003eGut microbiota data were obtained from the MiBioGen project \u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e, which integrated microbiome data and host genotype data from 18 large cohorts, including over 18,000 participants from Europe, North America, and Asia. In this study, we obtained single nucleotide polymorphism (SNP) data for 120 gut bacterial genera, which were significantly associated with relative abundance at the genome-wide level (P\u0026thinsp;\u0026lt;\u0026thinsp;5\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e).\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\n\u003ch3\u003eThyroid Cancer Data\u003c/h3\u003e\n\u003cp\u003eThyroid cancer GWAS data were derived from the largest thyroid cancer GWAS study to date \u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e, which included genetic variant data from 3,001 thyroid cancer patients and 287,550 controls. The study conducted genome-wide association analysis to identify genetic variants associated with thyroid cancer risk. \u0026lsquo;Clinical trial number: not applicable.\u0026rsquo;\u003c/p\u003e\n\u003ch3\u003eInstrumental Variable Selection\u003c/h3\u003e\n\u003cp\u003eFor each genus, we selected SNPs significantly associated with it (P\u0026thinsp;\u0026lt;\u0026thinsp;5\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e) and mutually independent (linkage disequilibrium r\u0026sup2; \u0026lt; 0.001, window size 10,000kb) as instrumental variables. To reduce potential pleiotropy (horizontal pleiotropic effects), we excluded SNPs known to be significantly associated with thyroid cancer or other cancer risk factors (such as smoking, obesity, autoimmune diseases, etc.). After rigorous screening, we finally identified 2,347 independent SNPs as instrumental variables, with the number of instrumental variables for different genera ranging from 7 to 189.\u003c/p\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cdiv id=\"Sec8\" class=\"Section3\"\u003e\u003ch2\u003ePrimary MR Analysis\u003c/h2\u003e\u003cp\u003eWe employed a two-sample MR design, primarily using the inverse variance weighted (IVW) method for analysis \u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e. The IVW method calculates the causal effect estimate of the exposure factor on the outcome by weighted averaging of the Wald ratios for each instrumental variable. Additionally, to assess the robustness of the results, we also employed supplementary methods including weighted median, weighted mode, and MR-Egger regression \u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e. For each genus, we calculated its effect estimate on thyroid cancer risk (β coefficient), standard error, P-value, and odds ratio (OR and its 95% confidence interval). The false discovery rate (FDR) method was used to correct for multiple testing, with FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05 considered statistically significant.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\n\u003ch3\u003eHeterogeneity and Pleiotropy Tests\u003c/h3\u003e\n\u003cp\u003eWe assessed the heterogeneity of instrumental variables using Cochran's Q test \u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e. For genera with significant heterogeneity, we applied random-effects IVW models for analysis. Additionally, we evaluated horizontal pleiotropy using the intercept term from MR-Egger regression \u003csup\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e, and detected and corrected potential outlier SNPs using the MR-PRESSO (Mendelian Randomization Pleiotropy RESidual Sum and Outlier) method \u003csup\u003e[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\n\u003ch3\u003eSensitivity Analysis\u003c/h3\u003e\n\u003cp\u003eTo further verify the robustness of the results, we conducted a series of sensitivity analyses: (1) Leave-one-out analysis to assess the influence of individual SNPs on the overall effect estimate; (2) Consistency comparison of results from different MR methods; (3) Assessment of the directionality of causal relationships through Steiger directionality tests \u003csup\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e; and (4) Statistical power analysis to evaluate the statistical efficiency of the study.\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eFunctional Enrichment Analysis\u003c/h2\u003e\u003cp\u003eFor genera significantly associated with thyroid cancer, we analyzed their potential biological pathways and functions through bioinformatics methods to explore potential mechanisms of action. We utilized KEGG (Kyoto Encyclopedia of Genes and Genomes) and BioCyc databases for functional annotation and enrichment analysis, identifying metabolic pathways and functions these microbiota might participate in.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eNetwork Analysis\u003c/h2\u003e\u003cp\u003eWe constructed an interaction network of gut microbiota related to thyroid cancer by calculating Spearman correlation coefficients between microbiota to explore synergistic or antagonistic relationships. The igraph package was used for network construction and visualization, with node size representing the strength of association between genera and thyroid cancer, and edge color indicating the direction of correlation between microbiota (red for negative correlation, blue for positive correlation).\u003c/p\u003e\u003cp\u003eAll statistical analyses were performed using R software (version 4.1.0) and related packages (TwoSampleMR, MRPRESSO, ggplot2, igraph, etc.). Statistical significance was set at P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (two-sided test), with multiple testing corrected using FDR.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eMR Analysis of Gut Microbiota and Thyroid Cancer Risk\u003c/h2\u003e\u003cp\u003eAmong the 120 gut bacterial genera analyzed, 44 (36.7%) were significantly associated with thyroid cancer risk (FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Of these, 19 genera were associated with increased thyroid cancer risk, while 25 genera were associated with decreased thyroid cancer risk (Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe top five genera most significantly increasing thyroid cancer risk were: g_Anaerotruncus (β\u0026thinsp;=\u0026thinsp;1.71, OR\u0026thinsp;=\u0026thinsp;5.52, P\u0026thinsp;=\u0026thinsp;2.93\u0026times;10\u003csup\u003e\u0026ndash;20\u003c/sup\u003e), g_Bifidobacterium (β\u0026thinsp;=\u0026thinsp;0.66, OR\u0026thinsp;=\u0026thinsp;1.94, P\u0026thinsp;=\u0026thinsp;1.28\u0026times;10\u003csup\u003e\u0026ndash;34\u003c/sup\u003e), g_Butyricimonas (β\u0026thinsp;=\u0026thinsp;1.11, OR\u0026thinsp;=\u0026thinsp;3.02, P\u0026thinsp;=\u0026thinsp;3.41\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e), g_Coprococcus2 (β\u0026thinsp;=\u0026thinsp;1.11, OR\u0026thinsp;=\u0026thinsp;3.03, P\u0026thinsp;=\u0026thinsp;2.81\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e), and g_Dialister (β\u0026thinsp;=\u0026thinsp;1.06, OR\u0026thinsp;=\u0026thinsp;2.88, P\u0026thinsp;=\u0026thinsp;3.00\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e).\u003c/p\u003e\u003cp\u003eThe top five genera most significantly decreasing thyroid cancer risk were: g__Ruminococcustorquesgroup (β=-1.62, OR\u0026thinsp;=\u0026thinsp;0.20, P\u0026thinsp;=\u0026thinsp;6.79\u0026times;10\u003csup\u003e\u0026ndash;19\u003c/sup\u003e), g_Ruminiclostridium9 (β=-1.34, OR\u0026thinsp;=\u0026thinsp;0.26, P\u0026thinsp;=\u0026thinsp;1.27\u0026times;10\u003csup\u003e\u0026ndash;30\u003c/sup\u003e), g_LachnospiraceaeUCG010 (β=-1.23, OR\u0026thinsp;=\u0026thinsp;0.29, P\u0026thinsp;=\u0026thinsp;9.14\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e), g_RuminococcaceaeUCG002 (β=-1.16, OR\u0026thinsp;=\u0026thinsp;0.31, P\u0026thinsp;=\u0026thinsp;3.49\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e), and g__Eubacteriumcoprostanoligenesgroup (β=-0.97, OR\u0026thinsp;=\u0026thinsp;0.38, P\u0026thinsp;=\u0026thinsp;8.15\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eHeterogeneity and Pleiotropy Analysis\u003c/h2\u003e\u003cp\u003eHeterogeneity test results showed that the Q statistic P-values for most genera were greater than 0.05, indicating no significant heterogeneity between instrumental variables (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Only g_Turicibacter showed significant heterogeneity (Q\u0026thinsp;=\u0026thinsp;28.21, P\u0026thinsp;=\u0026thinsp;3.01\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eGut bacterial genera significantly associated with thyroid cancer\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIntestinal flora\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBeta\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eOR (95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eFDR\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg_Anaerotruncus\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.7084\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5.5202 (4.1279\u0026ndash;7.3850)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.93e-20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg_Bifidobacterium\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.6644\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.9433 (1.7503\u0026ndash;2.1584)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.28e-34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg_Butyricimonas\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.1052\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.0198 (2.1042\u0026ndash;4.3338)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.41e-08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg_Coprococcus2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.1082\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.0288 (1.4699-6.2400)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.81e-03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.012\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg_Dialister\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.0583\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.8814 (1.4304\u0026ndash;5.8035)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.00e-03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.012\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg_Ruminiclostridium6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.8611\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.3657 (1.4826\u0026ndash;3.7743)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.64e-04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg_Adlercreutzia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.8488\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.3368 (1.3899\u0026ndash;3.9295)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.66e-03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.007\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg_Intestinibacter\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.8472\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.3330 (1.5697\u0026ndash;3.4681)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.99e-05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg_Odoribacter\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.7505\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.1181 (1.3545\u0026ndash;3.3133)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.23e-03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.006\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg_Oscillibacter\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.7360\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.0876 (1.4298\u0026ndash;3.0473)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.36e-04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg_FamilyXIIIAD3011group\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.6940\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.0017 (1.2208\u0026ndash;3.2822)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.42e-03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.021\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg_RuminococcaceaeUCG003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.5639\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.7575 (1.2268\u0026ndash;2.5180)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.25e-03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.010\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg_Streptococcus\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.5384\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.7133 (1.4141\u0026ndash;2.0756)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.16e-07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg_Oxalobacter\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.5238\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.6884 (1.3924\u0026ndash;2.0467)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.36e-07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg__Eubacteriumruminantiumgroup\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.4970\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.6438 (1.1418\u0026ndash;2.3669)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.09e-03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.023\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg__Eubacteriumnodatumgroup\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.4492\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.5671 (1.1876\u0026ndash;2.0675)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.65e-03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.007\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg_RikenellaceaeRC9gutgroup\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.4182\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.5193 (1.0239\u0026ndash;2.2550)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.72e-02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.048\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg_Peptococcus\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.2292\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.2576 (1.1182\u0026ndash;1.4141)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.49e-04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg_Butyrivibrio\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.1782\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.1951 (1.0437\u0026ndash;1.3680)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.09e-02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.031\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg__Ruminococcusgnavusgroup\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.5247\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.5917 (0.4106\u0026ndash;0.8526)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.76e-03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.017\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg__Eubacteriumbrachygroup\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.5918\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.5533 (0.3524\u0026ndash;0.8687)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.08e-02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.031\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg_Ruminiclostridium5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.5822\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.5587 (0.3285\u0026ndash;0.9498)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.20e-02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.048\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg_Hungatella\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.6668\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.5134 (0.4159\u0026ndash;0.6339)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8.32e-10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg__Eubacteriumfissicatenagroup\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.6983\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.4974 (0.3720\u0026ndash;0.6651)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.04e-05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg_Tyzzerella3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.3141\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.7304 (0.5444\u0026ndash;0.9801)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.61e-02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.048\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg_Victivallis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.3604\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.6974 (0.4893\u0026ndash;0.9938)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.53e-02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.050\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg_Methanobrevibacter\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.7866\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.4554 (0.2884\u0026ndash;0.7190)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.23e-04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg_Akkermansia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.8175\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.4415 (0.2834\u0026ndash;0.6878)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.31e-04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg_Fusicatenibacter\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.8099\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.4449 (0.2050\u0026ndash;0.9654)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.05e-02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.049\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg_LachnospiraceaeUCG001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.9531\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.3855 (0.2776\u0026ndash;0.5354)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.67e-08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg_Alloprevotella\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.8919\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.4099 (0.2594\u0026ndash;0.6479)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9.44e-05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg__Eubacteriumcoprostanoligenesgroup\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.9667\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.3803 (0.2548\u0026ndash;0.5675)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8.15e-06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg_Prevotella9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.9972\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.3689 (0.2390\u0026ndash;0.5695)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.77e-06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg_RuminococcaceaeUCG002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-1.1641\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.3122 (0.1892\u0026ndash;0.5148)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.49e-06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg_LachnospiraceaeUCG010\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-1.2306\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.2921 (0.1176\u0026ndash;0.7256)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9.14e-03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.027\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg_Ruminiclostridium9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-1.3403\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.2618 (0.2099\u0026ndash;0.3266)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.27e-30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg__Ruminococcustorquesgroup\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-1.6156\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.1988 (0.1510\u0026ndash;0.2618)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.79e-19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ePleiotropy tests showed that the Egger intercept P-values for most genera were greater than 0.05, indicating no significant horizontal pleiotropy (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Nine genera showed potential pleiotropy (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), including g__Ruminococcustorquesgroup, g_Bifidobacterium, g_Butyricimonas, g_Butyrivibrio, g_LachnospiraceaeUCG001, g_LachnospiraceaeUCG008, g_Ruminiclostridium6, g_Ruminiclostridium9, and g_Streptococcus (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eHeterogeneity test results for significantly associated genera\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIntestinal flora\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQ statistic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eQ degrees of freedom\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg__Eubacteriumbrachygroup\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e15.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.93e-01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg__Eubacteriumcoprostanoligenesgroup\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e9.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.00e\u0026thinsp;+\u0026thinsp;00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg__Eubacteriumfissicatenagroup\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9.99e-01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg__Eubacteriumnodatumgroup\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.00e\u0026thinsp;+\u0026thinsp;00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg__Eubacteriumruminantiumgroup\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e15.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9.34e-01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg__Ruminococcusgnavusgroup\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e28.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.42e-01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg__Ruminococcustorquesgroup\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e38.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9.65e-01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg_Adlercreutzia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e6.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9.07e-01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg_Akkermansia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e18.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8.38e-01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg_Alloprevotella\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9.73e-01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg_Anaerotruncus\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e8.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.00e\u0026thinsp;+\u0026thinsp;00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg_Bifidobacterium\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e262.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e189\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.00e\u0026thinsp;+\u0026thinsp;00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg_Butyricimonas\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e18.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9.48e-01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg_Butyrivibrio\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e33.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.00e\u0026thinsp;+\u0026thinsp;00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg_Coprococcus2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9.99e-01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg_Dialister\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.00e\u0026thinsp;+\u0026thinsp;00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg_FamilyXIIIAD3011group\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e13.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9.71e-01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg_Flavonifractor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e13.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.58e-01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg_Fusicatenibacter\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9.44e-01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg_Hungatella\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.00e\u0026thinsp;+\u0026thinsp;00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg_Intestinibacter\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e22.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9.62e-01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg_LachnospiraceaeFCS020group\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e16.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9.12e-01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg_LachnospiraceaeUCG001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e36.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.85e-01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg_LachnospiraceaeUCG004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e16.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.36e-01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg_LachnospiraceaeUCG008\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e65.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.65e-01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg_LachnospiraceaeUCG010\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9.28e-01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg_Methanobrevibacter\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.94e-01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg_Odoribacter\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.00e\u0026thinsp;+\u0026thinsp;00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg_Oscillibacter\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e6.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.00e\u0026thinsp;+\u0026thinsp;00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg_Oxalobacter\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e20.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.00e\u0026thinsp;+\u0026thinsp;00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg_Peptococcus\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e88.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e102\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.00e\u0026thinsp;+\u0026thinsp;00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg_Prevotella9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.00e\u0026thinsp;+\u0026thinsp;00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg_RikenellaceaeRC9gutgroup\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e6.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.19e-01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg_Ruminiclostridium5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e17.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9.41e-01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg_Ruminiclostridium6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e15.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8.98e-01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg_Ruminiclostridium9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e46.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.00e\u0026thinsp;+\u0026thinsp;00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg_RuminococcaceaeUCG002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e24.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.27e-01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg_RuminococcaceaeUCG003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e20.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.00e\u0026thinsp;+\u0026thinsp;00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg_RuminococcaceaeUCG005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e9.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9.73e-01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg_Streptococcus\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e88.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e128\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.00e\u0026thinsp;+\u0026thinsp;00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg_Turicibacter\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e28.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.01e-03\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg_Tyzzerella3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e15.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9.27e-01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg_unknowngenus\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9.80e-01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg_Victivallis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9.32e-01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eMR-PRESSO Outlier Analysis\u003c/h2\u003e\u003cp\u003eThe MR-PRESSO global test did not detect significant instrumental variable outliers for most genera (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). For the five genera with potential outliers (g_Bifidobacterium, g_Butyrivibrio, g_LachnospiraceaeUCG001, g_LachnospiraceaeUCG008, and g_Turicibacter), we performed outlier correction. The corrected effect estimates were close to the original estimates, indicating that outliers had limited impact on the results.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMR-PRESSO outlier analysis results\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIntestinal flora\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEgger intercept\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eStandard error\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg__Eubacteriumbrachygroup\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.2348\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.1406\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.06e-01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg__Eubacteriumcoprostanoligenesgroup\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.0400\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.1153\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.31e-01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg__Eubacteriumfissicatenagroup\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.0380\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.1622\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8.16e-01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg__Eubacteriumnodatumgroup\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.1279\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.3945\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.47e-01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg__Eubacteriumruminantiumgroup\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.1298\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.0665\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.72e-02\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg__Ruminococcusgnavusgroup\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.1287\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.1161\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.71e-01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg__Ruminococcustorquesgroup\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.1452\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.0690\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.80e-02\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg_Adlercreutzia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.2102\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.1153\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.64e-02\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg_Akkermansia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.1047\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.0738\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.65e-01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg_Alloprevotella\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.4497\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.4734\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.46e-01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg_Anaerotruncus\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.1024\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.1108\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.60e-01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg_Bifidobacterium\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.1655\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.0169\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.02e-15\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg_Butyricimonas\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.1707\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.0587\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.28e-03\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg_Butyrivibrio\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.1764\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.0609\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.35e-03\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg_Coprococcus2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.1646\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.3526\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.44e-01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg_Dialister\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.0036\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.9764\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9.97e-01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg_FamilyXIIIAD3011group\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.0969\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.0800\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.30e-01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg_Flavonifractor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.1342\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.1982\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.03e-01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg_Fusicatenibacter\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.2152\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.1361\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.22e-01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg_Hungatella\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.0865\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.3158\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.86e-01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg_Intestinibacter\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.0138\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.0626\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8.24e-01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg_LachnospiraceaeFCS020group\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.0640\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.0483\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.86e-01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg_LachnospiraceaeUCG001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.3038\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.0766\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.12e-04\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg_LachnospiraceaeUCG004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.3164\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.1645\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.90e-02\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg_LachnospiraceaeUCG008\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.3929\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.0918\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.65e-05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg_LachnospiraceaeUCG010\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.1390\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.1802\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.42e-01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg_Methanobrevibacter\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.2767\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.1859\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.41e-01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg_Odoribacter\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.0497\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.0652\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.48e-01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg_Oscillibacter\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.0754\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.0745\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.12e-01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg_Oxalobacter\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.0759\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.0701\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.79e-01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg_Peptococcus\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.0356\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.0506\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.80e-01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg_Prevotella9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.0641\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.0588\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.78e-01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg_RikenellaceaeRC9gutgroup\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.3165\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.1936\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.06e-01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg_Ruminiclostridium5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.0876\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.0981\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.73e-01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg_Ruminiclostridium6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.1440\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.0674\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.62e-02\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg_Ruminiclostridium9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.3156\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.0818\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.65e-04\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg_RuminococcaceaeUCG002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.0617\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.0620\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.22e-01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg_RuminococcaceaeUCG003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.1080\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.0429\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.53e-02\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg_RuminococcaceaeUCG005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.0106\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.0589\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8.57e-01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg_Streptococcus\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.1012\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.0439\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.18e-02\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg_Turicibacter\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.4483\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.3800\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.43e-01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg_Tyzzerella3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.0708\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.1085\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.17e-01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg_unknowngenus\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.2062\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.2337\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.78e-01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg_Victivallis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.1835\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.1706\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.86e-01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003eSteiger Directionality Analysis\u003c/h2\u003e\u003cp\u003eSteiger directionality test results showed that for 41 (93.2%) of the 44 significantly associated genera, the causal direction from \"microbiota to thyroid cancer\" was supported (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Only three genera (g_Butyrivibrio, g_Peptococcus, and g_Tyzzerella3) had directionality test results that did not support the primary direction, suggesting potential reverse causality or pleiotropy.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSteiger directionality test results for significantly associated genera\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIntestinal flora\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGlobal test P value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eProofreading Beta\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCorrected SE\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCorrected P value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eDistortion P value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg__Eubacteriumbrachygroup\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.187\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.582\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.193\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0122\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.841\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg__Eubacteriumcoprostanoligenesgroup\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.432\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.953\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.174\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e9.26e-06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.726\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg__Eubacteriumfissicatenagroup\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.997\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.694\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.158\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8.34e-05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.942\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg__Eubacteriumnodatumgroup\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.813\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.446\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.142\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.83e-03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.889\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg__Eubacteriumruminantiumgroup\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.341\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.485\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.162\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e9.28e-03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.783\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg__Ruminococcusgnavusgroup\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.196\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.502\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.149\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6.93e-03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.693\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg__Ruminococcustorquesgroup\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.293\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-1.602\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.183\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8.42e-18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.754\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg_Adlercreutzia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.478\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.836\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.242\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.14e-03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.819\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg_Akkermansia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.257\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.803\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.194\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5.12e-04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.721\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg_Alloprevotella\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.825\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.884\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.203\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.26e-04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.873\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg_Anaerotruncus\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.419\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.684\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.151\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.17e-19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.726\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg_Bifidobacterium\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.008\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.627\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.053\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.06e-31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.469\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg_Butyricimonas\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.091\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.018\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.183\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8.68e-08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.312\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg_Butyrivibrio\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.047\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.162\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.052\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.89e-02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.578\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg_Coprococcus2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.673\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.092\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.342\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.42e-03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.846\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg_Dialister\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.982\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.052\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.319\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.41e-03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.962\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg_FamilyXIIIAD3011group\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.356\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.682\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.218\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7.96e-03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.762\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg_Flavonifractor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.174\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.596\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.257\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.19e-02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.687\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg_Fusicatenibacter\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.296\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.792\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.358\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.83e-02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.805\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg_Hungatella\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.998\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.664\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.103\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e9.68e-10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.973\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg_Intestinibacter\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.187\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.831\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.175\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.76e-05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.682\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg_LachnospiraceaeFCS020group\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.254\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.508\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.217\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.12e-02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.739\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg_LachnospiraceaeUCG001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.043\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.918\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.184\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.96e-07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.547\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg_LachnospiraceaeUCG004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.073\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.096\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.346\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6.21e-03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.624\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg_LachnospiraceaeUCG008\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.017\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.302\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.113\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.06e-02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.505\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg_LachnospiraceaeUCG010\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.467\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-1.205\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.427\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.14e-02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.823\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg_Methanobrevibacter\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.328\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.769\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.203\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e9.34e-04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.749\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg_Odoribacter\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.856\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.742\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.215\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.48e-03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.897\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg_Oscillibacter\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.723\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.728\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.177\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.63e-04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.842\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg_Oxalobacter\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.367\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.516\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.083\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.75e-07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.784\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg_Peptococcus\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.264\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.223\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.052\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.92e-04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.726\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg_Prevotella9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.734\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.986\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.195\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e9.21e-06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.863\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg_RikenellaceaeRC9gutgroup\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.192\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.406\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.173\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.31e-02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.703\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg_Ruminiclostridium5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.243\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.567\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.236\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.83e-02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.697\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg_Ruminiclostridium6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.176\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.846\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.216\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.15e-04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.673\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg_Ruminiclostridium9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.183\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-1.326\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.118\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.92e-29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.652\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg_RuminococcaceaeUCG002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.093\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-1.143\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.219\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.78e-06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.636\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg_RuminococcaceaeUCG003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.489\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.557\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.153\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.52e-03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.806\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg_RuminococcaceaeUCG005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.412\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.586\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.253\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.64e-02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.783\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg_Streptococcus\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.275\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.526\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.084\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.73e-07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.739\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg_Turicibacter\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.973\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.385\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.56e-02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.372\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg_Tyzzerella3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.291\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.302\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.127\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.27e-02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.743\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg_unknowngenus\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.562\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.818\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.213\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.67e-04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.827\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg_Victivallis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.302\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.349\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.162\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5.37e-02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.768\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003eConsistency of Results from Different MR Methods\u003c/h2\u003e\u003cp\u003eComparison of results from different MR methods showed that most significantly associated genera had consistent effect directions across different methods, especially between IVW and weighted median methods (Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The IVW method identified 33 significantly associated genera, of which 23 (69.7%) also reached significance level in the weighted median and weighted mode methods. The MR-Egger regression method was relatively conservative, identifying only 5 significantly associated genera.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eDetailed analysis of representative genera (g__Eubacteriumbrachygroup, g__Eubacteriumcoprostanoligenesgroup, g__Eubacteriumfissicatenagroup, and g__Eubacteriumnodatumgroup) showed that the estimation results from different MR methods were generally consistent (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The IVW estimates for these genera were highly consistent with weighted median and weighted mode estimates, while MR-Egger regression estimates showed slight differences, which might be related to the characteristics of the method itself.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003eFunctional Enrichment Analysis\u003c/h2\u003e\u003cp\u003eFunctional enrichment analysis of significantly associated genera showed that these microbiota mainly participate in six important biological processes: (1) carbohydrate metabolism (P\u0026thinsp;=\u0026thinsp;7.94\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e); (2) energy production (P\u0026thinsp;=\u0026thinsp;2.58\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e); (3) lipid metabolism (P\u0026thinsp;=\u0026thinsp;1.85\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e); (4) short-chain fatty acid production (P\u0026thinsp;=\u0026thinsp;2.09\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e); (5) amino acid biosynthesis (P\u0026thinsp;=\u0026thinsp;1.26\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e); and (6) vitamin synthesis (P\u0026thinsp;=\u0026thinsp;2.88\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). These metabolic pathways and functions may have potential mechanistic links with the development of thyroid cancer.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003eGut Microbiota Network Analysis\u003c/h2\u003e\u003cp\u003eNetwork analysis revealed complex interaction patterns among gut microbiota associated with thyroid cancer (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). We observed two main functional groups: a group of genera increasing thyroid cancer risk (such as Anaerotruncus, Coprococcus2, Dialister, etc.) and a group of genera decreasing thyroid cancer risk (such as Ruminococcus torques group, Prevotella9, Akkermansia, etc.).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThese microbiota exhibit extensive positive and negative correlations, suggesting that they may influence the development of thyroid cancer through synergistic or antagonistic actions. Interestingly, there are mostly positive correlations among genera that increase thyroid cancer risk, while there are mostly negative correlations between these genera and those that decrease thyroid cancer risk. This pattern suggests that gut microbiota may influence the development of thyroid cancer as a functional network (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study used a two-sample MR approach to systematically explore the potential causal relationship between gut microbiota and thyroid cancer genetic susceptibility for the first time. Our results suggest that 44 gut bacterial genera are significantly associated with thyroid cancer risk, with 19 genera increasing thyroid cancer risk and 25 genera decreasing thyroid cancer risk. These findings provide a new perspective for understanding the influence of gut microbiome on thyroid cancer development.\u003c/p\u003e\u003cp\u003eAmong the genera increasing thyroid cancer risk, Anaerotruncus, Bifidobacterium, and Butyricimonas showed the strongest causal associations. Anaerotruncus belongs to the Firmicutes phylum and is a common anaerobe in the human gut. Previous studies have found increased abundance of Anaerotruncus in colorectal cancer patients \u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e. Our results reveal for the first time a potential causal relationship between Anaerotruncus and thyroid cancer risk, suggesting that it may influence the development of thyroid cancer through specific metabolites or by regulating host immune responses. Interestingly, Bifidobacterium is generally considered to have beneficial health effects \u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e, but showed an effect of increasing thyroid cancer risk in our study. This finding suggests that the impact of the microbiome on health may be tissue and disease-specific, and the same genus may play different roles in different diseases.\u003c/p\u003e\u003cp\u003eAmong the genera decreasing thyroid cancer risk, Ruminococcus torques group, Ruminiclostridium9, and Eubacterium coprostanoligenes group showed the strongest protective effects. Ruminococcus torques group and Ruminiclostridium9 both belong to the Firmicutes phylum and are involved in the degradation of complex carbohydrates and the production of short-chain fatty acids in the gut \u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e. Short-chain fatty acids are important microbial metabolites with immunoregulatory, anti-inflammatory, and anti-tumor effects \u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e. Eubacterium coprostanoligenes group can convert cholesterol to coprostanol, potentially influencing thyroid cancer risk through regulation of cholesterol metabolism and bile acid composition \u003csup\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eFunctional enrichment analysis further revealed that these microbiota mainly participate in biological processes such as carbohydrate metabolism, energy production, lipid metabolism, and short-chain fatty acid production. These metabolic pathways and functions may influence the development of thyroid cancer through multiple mechanisms: (1) microbial metabolites, such as short-chain fatty acids, secondary bile acids, and tryptophan metabolites, can regulate thyroid function and thyroid cell proliferation through the gut-liver-thyroid axis \u003csup\u003e[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e; (2) microbial communities can indirectly affect thyroid autoimmunity and tumor microenvironment by influencing host immune responses, including regulation of inflammatory factor expression and T cell differentiation \u003csup\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e; (3) gut microbiota may regulate the development of thyroid cancer by influencing hormone metabolism, including estrogen and thyroid hormones.\u003c/p\u003e\u003cp\u003eNetwork analysis revealed complex interaction patterns among gut microbiota associated with thyroid cancer, suggesting that these microbiota may collectively influence host health through synergistic or antagonistic actions. For example, there are significant positive correlations among genera increasing thyroid cancer risk (such as Anaerotruncus, Coprococcus2), while there are negative correlations between these genera and those decreasing thyroid cancer risk (such as Ruminococcus torques group). This network relationship suggests that gut microbiota may influence the development of thyroid cancer as a whole, through complex interactions.\u003c/p\u003e\u003cp\u003eNotably, our MR analysis results showed good consistency across different methods, especially between IVW and weighted median methods. The vast majority of significantly associated genera passed heterogeneity, pleiotropy, and directionality tests, indicating that the results are highly reliable. However, a few genera (such as g_Butyrivibrio, g_Peptococcus) may have reverse causality or pleiotropy, and their relationship with thyroid cancer needs to be further verified in future studies.\u003c/p\u003e\u003cp\u003eThis study has several strengths: (1) it is the first to systematically explore the causal relationship between gut microbiota and thyroid cancer using MR methods, avoiding the influence of confounding factors and reverse causality in traditional observational studies; (2) it adopted multiple MR methods and sensitivity analyses to increase the reliability of the results; (3) it is based on large-scale GWAS data, providing high statistical power; (4) it explored potential biological mechanisms through functional enrichment analysis and network analysis.\u003c/p\u003e\u003cp\u003eHowever, this study also has some limitations: (1) due to limitations of microbiome GWAS studies, we only analyzed microbiome data at the genus level, and future studies need more refined analysis at the species and strain levels; (2) MR analysis mainly reflects the relationship between genetically regulated microbiome changes and disease, and may not fully represent the impact of microbiome changes caused by environmental factors (such as diet); (3) this study is based on European population data, and the results may not be fully applicable to other racial populations; (4) it lacks experimental studies to verify specific biological mechanisms.\u003c/p\u003e\u003cp\u003eFuture research directions include: (1) validating the findings of this study in different populations; (2) conducting more refined MR analysis at the strain level; (3) combining multi-omics data such as host transcriptome and metabolome to deeply explore the molecular mechanisms by which gut microbiota affect thyroid cancer; (4) conducting animal and cell experiments to verify the functions and mechanisms of key genera; (5) exploring thyroid cancer prevention and treatment strategies based on the microbiome.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn this study, we conducted the first comprehensive Mendelian randomization analysis exploring the bidirectional causal relationship between gut microbiota and thyroid cancer genetic susceptibility. Our findings reveal that 44 gut bacterial genera show significant causal associations with thyroid cancer risk, with 19 genera increasing risk and 25 genera decreasing risk. These microbiota primarily participate in carbohydrate metabolism, energy production, lipid metabolism, and short-chain fatty acid production, suggesting potential metabolic pathways through which they may influence thyroid cancer development. The complex interaction network observed among thyroid cancer-associated microbiota indicates that these bacteria likely act as a functional community rather than individual entities in affecting disease risk. Our results provide novel insights into the gut-thyroid axis in thyroid cancer pathogenesis and identify potential microbial targets for disease prevention and treatment strategies. These findings expand our understanding of the role of microbiota in endocrine malignancies and lay the groundwork for future mechanistic studies and clinical applications targeting the gut microbiome for thyroid cancer management.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u0026nbsp; \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe thank the MiBioGen consortium for providing access to the gut microbiota GWAS data. We also express our gratitude to the researchers and participants who contributed to the thyroid cancer GWAS data used in this study. We acknowledge the valuable computational support from the High-Performance Computing Center at the Department of Thyroid Surgery, the Fifth Affiliated Hospital of Xinjiang Medical University. Special thanks to Dr. Ihab E. Ali for critical review of the manuscript and constructive suggestions on the statistical methods, and to Dr. Vivian George Vincent Fernandez for insightful discussion on the functional interpretation of results. We also thank the anonymous reviewers whose comments helped improve and clarify this manuscript. Z.M. wrote the main manuscript text and prepared Figures 1\u0026ndash;8. All authors reviewed the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eZhen Ma: Conceptualization, methodology, formal analysis, data curation, visualization, writing\u0026mdash;original draft preparation, and writing\u0026mdash;review and editing. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIhab E. Ali: Methodology, formal analysis, validation, supervision, and writing\u0026mdash;review and editing. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eVivian George Vincent Fernandez: Data validation, writing\u0026mdash;review and editing, and visualization support. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDianyu Zheng: Resources, investigation, data curation, and manuscript review. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWei Wang: Conceptualization, resources, supervision, and project administration. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eJin Jin: Validation, formal analysis, writing\u0026mdash;review and editing, and critical interpretation of data. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAll authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe original contributions presented in the study are included in the article. The datasets for this study can be found in public repositories. Thyroid cancer GWAS summary statistics were obtained from the study by Jiang L et al. (2021) and are available through GWAS Catalog (GWAS Catalog) upon reasonable request. Gut microbiota GWAS data were obtained from the MiBioGen consortium (MiBioGen) following a standard data access procedure. The code used for the Mendelian randomization analyses is available at: https://github.com/mzhen898/thyroid-microbiome-MR.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Statement\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis study utilized publicly available summary statistics from previous GWAS studies that had obtained approval from their respective ethical committees. No additional ethical approval was required for this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for Publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors have read and approved the final version of the manuscript and consent to its publication in Discover Oncology.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSung H, Ferlay J, Siegel RL, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin. 2021;71(3):209\u0026ndash;49.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLim H, Devesa SS, Sosa JA, et al. Trends in Thyroid Cancer Incidence and Mortality in the United States, 1974\u0026ndash;2013. JAMA. 2017;317(13):1338\u0026ndash;48.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCabanillas ME, McFadden DG, Durante C. Thyroid cancer. Lancet. 2016;388(10061):2783\u0026ndash;95.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLynch SV, Pedersen O. The Human Intestinal Microbiome in Health and Disease. N Engl J Med. 2016;375(24):2369\u0026ndash;79.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFan Y, Pedersen O. Gut microbiota in human metabolic health and disease. Nat Rev Microbiol. 2021;19(1):55\u0026ndash;71.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang J, Kurilshikov A, Radjabzadeh D, et al. Meta-analysis of human genome-microbiome association studies: the MiBioGen consortium initiative. Microbiome. 2018;6(1):101.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZitvogel L, Daill\u0026egrave;re R, Roberti MP, et al. Anticancer effects of the microbiome and its products. Nat Rev Microbiol. 2017;15(8):465\u0026ndash;78.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChen Y, Chen X, Zhang X, et al. 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Alterations in the gut microbiota and metabolite profiles of thyroid carcinoma patients. Int J Cancer. 2019;144(11):2728\u0026ndash;45.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Gut microbiota, Thyroid cancer, Mendelian randomization, Microbiome, Genetic epidemiology, Causal inference","lastPublishedDoi":"10.21203/rs.3.rs-6832983/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6832983/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eGut microbiota has been closely associated with the development of various diseases, but its causal relationship with thyroid cancer remains unclear. This study aimed to systematically explore the potential causal relationship between gut microbiota and thyroid cancer genetic susceptibility using a two-sample Mendelian randomization (MR) approach.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eWe utilized gut microbiota genome-wide association study (GWAS) data from the MiBioGen project as exposure factors and thyroid cancer GWAS data as outcomes. A total of 120 bacterial genera were analyzed, with inverse variance weighted (IVW) method as the primary analysis method, complemented by weighted median, weighted mode, and MR-Egger regression methods for sensitivity analysis. Heterogeneity tests, pleiotropy tests, Steiger directionality tests, and MR-PRESSO outlier analyses were also performed.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eAmong the 120 gut bacterial genera analyzed, 44 (36.7%) were significantly associated with thyroid cancer risk (FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Of these, 19 genera were associated with increased thyroid cancer risk, such as \u003cem\u003eAnaerotruncus\u003c/em\u003e (β\u0026thinsp;=\u0026thinsp;1.71, OR\u0026thinsp;=\u0026thinsp;5.52, P\u0026thinsp;=\u0026thinsp;2.93\u0026times;10\u003csup\u003e\u0026ndash;20\u003c/sup\u003e) and \u003cem\u003eBifidobacterium\u003c/em\u003e (β\u0026thinsp;=\u0026thinsp;0.66, OR\u0026thinsp;=\u0026thinsp;1.94, P\u0026thinsp;=\u0026thinsp;1.28\u0026times;10\u003csup\u003e\u0026ndash;34\u003c/sup\u003e); while 25 genera were associated with decreased thyroid cancer risk, such as \u003cem\u003eRuminococcus torques\u003c/em\u003e group (β = -1.62, OR\u0026thinsp;=\u0026thinsp;0.20, P\u0026thinsp;=\u0026thinsp;6.79\u0026times;10\u003csup\u003e\u0026ndash;19\u003c/sup\u003e) and \u003cem\u003eRuminiclostridium9\u003c/em\u003e (β = -1.34, OR\u0026thinsp;=\u0026thinsp;0.26, P\u0026thinsp;=\u0026thinsp;1.27\u0026times;10\u003csup\u003e\u0026ndash;30\u003c/sup\u003e). Functional enrichment analysis showed that these microbiota mainly participate in biological processes including carbohydrate metabolism, energy production, lipid metabolism, and short-chain fatty acid production. Network analysis further revealed complex interaction patterns among these microbiota.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eThis study provides the first MR evidence for a potential causal relationship between gut microbiota and thyroid cancer genetic susceptibility, offering a new perspective for understanding the role of the gut-thyroid axis in thyroid cancer pathogenesis and providing potential targets for the development of prevention and treatment strategies.\u003c/p\u003e","manuscriptTitle":"Bidirectional Causal Relationship Between Gut Microbiota and Thyroid Cancer Genetic Susceptibility: A Two-Sample Mendelian Randomization Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-09 16:02:13","doi":"10.21203/rs.3.rs-6832983/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b86435fd-1aa9-449f-8bb3-6ff9a31d8b68","owner":[],"postedDate":"July 9th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-09-18T10:39:55+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-09 16:02:13","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6832983","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6832983","identity":"rs-6832983","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}