Causal effects of the gut microbiome on immune-related vasculitis: A two-sample Mendelian randomization study

Causal effects of the gut microbiome on immune-related vasculitis: 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 Article Causal effects of the gut microbiome on immune-related vasculitis: A two-sample Mendelian randomization study Si Chen, Rui Nie, Chao Wang, Haixia Luan, Xu Ma, Yuan Gui, Xiaoli Zeng, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3874319/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 13 Aug, 2024 Read the published version in Scientific Reports → Version 1 posted 12 You are reading this latest preprint version Abstract Observational data suggest a link between gut microbiota and immune-related vasculitis, but causality remains unclear. A bidirectional mendelian randomization (MR) study was conducted using public genome-wide data. The inverse-variance-weighted (IVW) method identified associations and addressed heterogeneity. Families Clostridiaceae 1 and Actinomycetaceae correlated positively with granulomatosis with polyangiitis risk, while classes Lentisphaeria and Melainabacteria , and families Lachnospiraceae and Streptococcaceae showed negative associations. Behçet's disease was positively associated with the risk of family Streptococcaceae abundance. And other several gut microbiota constituents were identified as potential risk factors for immune-related vasculitis. Furthermore, combining positive association results from the IVW analysis revealed numerous shared gut microbiota constituents associated with immune-related vasculitis. MR analysis demonstrated a causal association between the gut microbiota and immune-related vasculitis, offering valuable insights for subsequent mechanistic and clinical investigations into microbiota-mediated immune-related vasculitis. Biological sciences/Immunology/Autoimmunity Biological sciences/Microbiology/Microbial genetics/Bacterial genetics gut microbiota immune-related vasculitis mendelian randomization GPA SNPs Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction Vasculitis is an autoimmune disorder characterized primarily by predominant vascular inflammation and destruction. Our study selectively encompasses specific vasculitis types from the 2012 Chapel Hill Nomenclature [ 1 ]. We focused solely on studies with accessible genome-wide association study (GWAS) summary data related to giant cell arteritis (GCA), behçet's disease (BD), kawasaki disease (KD), and granulomatosis with polyangiitis (GPA). GCA is a systemic vasculitis distinguished by granulomatous inflammation affecting large and medium-sized arteries, which predominantly occurs in individuals aged 50 years or older, with an incidence rate of approximately 10 cases per 100,000 people [ 2 ]. The manifestation of GCA involves granulomatous inflammation, particularly in the temporal artery, resulting in symptoms such as headache, jaw claudication, scalp discomfort, and increased C-reactive protein levels and erythrocyte sedimentation rate [ 3 ]. BD is a rare autoimmune disorder that causes inflammation and lesions in various body systems. It involves oral and genital ulcers, eye inflammation, and vascular issues and affects multiple organs [ 4 ]. BD is characterized by persistent mucosal ulceration and systemic vasculitis and has demographic and clinical heterogeneity. Mortality is elevated due to complications in the pulmonary artery, large vessels, nerves, and gastrointestinal tract [ 5 ]. KD manifests as an acute febrile illness and systemic vasculitis that primarily affects young children typically ranging from 6 months to 4 years of age [ 6 ]. Initially perceived as self-limiting [ 7 ], KD is now acknowledged as a systemic vasculitis with a propensity to cause the development of coronary artery lesions, affecting up to 25% of untreated patients [ 8 ]. The emergence of coronary artery lesions associated with KD is a major contributor to pediatric heart disease in developed nations. GPA, formerly known as wegner's granulomatosis, represents a rare form of necrotizing vasculitis affecting small- to medium-sized vessels. This condition predominantly affects the upper and lower respiratory tracts, as well as the renal system [ 9 ]. The formation of necrotizing granulomas in regions around the head, neck, and kidneys can result in diverse and numerous clinical manifestations. Gut dysbiosis, which influences mucosal immune homeostasis and gut barrier integrity, may contribute to autoimmune disorders. The established link between the microbiome and autoimmune diseases, such as systemic lupus erythematosus and rheumatoid arthritis, offers a promising therapeutic target [ 10 ]. Vasculitis patients exhibit dysbiosis compared to those of individuals with healthy controls. Crucially, environmental triggers, including changes in the gut microbiota, play a significant role in vasculitis onset, with an imbalance in the intestinal microbiome occurring with an increased abundance of pathogenic bacteria and a decreased abundance of beneficial bacteria [ 11 ]. Nevertheless, investigations of the microbiome in blood and the aorta have revealed varying abundances of Actinobacteria , Proteobacteria , Bifidobacterium , and Parasutterella and minimal Bacteroidetes , Rhodococcus , Cytophagaceae , and Granulicatella among GCA patients [ 12 – 15 ]. Several cross-sectional studies have indicated alterations in gut bacterial abundance in patients with BD compared to healthy controls. These findings revealed an enrichment of lactic acid-producing bacteria, sulfate-reducing bacteria, and certain opportunistic pathogens in the gut microbiota of BD patients; conversely, there was a deficiency in butyric acid-producing bacteria and methanogenic bacteria [ 16 – 24 ]. KD arises from the interplay of genetic and environmental susceptibility factors alongside infectious triggers. Several researchers have proposed that KD induces microbial dysbiosis, diminishing the production of short-chain fatty acids (SCFAs) by the intestinal microbiota. This alteration potentially leads to an imbalance between helper T cells 17 and regulatory T cells, contributing to the pathogenesis of KD [ 25 , 26 ]. Dekkema et al. conducted a comprehensive review of nasal microbiome studies in patients with ANCA-associated vasculitis (AAV), encompassing those with GPA. They found that nasal microbial dysbiosis is prevalent in active AAV patients, and immunosuppressive treatment thus has the potential to ameliorate this disturbance [ 27 ]. Notably, investigations on GPA and the gut microbiota are lacking. The inherent limitations within the designs of observational studies pose challenges in establishing definitive causal relationships. Constraints such as potential influence from confounding variables and biases, limited sample sizes, and variations in ethnic demographics hinder the ability to conclusively establish causality. Consequently, the causative nature of the relationship between the gut microbiota and immune-related vasculitis remains unclear. Furthermore, the directionality of this relationship—whether accidental, bidirectional, or unidirectional—remains ambiguous. Mendelian randomization (MR) is a contemporary epidemiological approach employed when the execution of randomized controlled trials is impractical. The primary data source for MR is derived from the Human Genome Project, and genetic variants are utilized as instrumental variables (IVs) to mitigate the limitations inherent in observational studies. By extracting single-nucleotide polymorphisms (SNPs) from GWASs, MR establishes a link between exposure and outcome while employing analytical techniques to mitigate confounding factors [ 28 – 30 ]. MR investigations, akin to randomized controlled trials, yield results less susceptible to reverse causation and residual bias [ 31 ]. An increase in the number of GWASs related to the gut microbiota and disease [ 32 , 33 ] has led to the widespread availability of large-scale summary statistics, facilitating two-sample MR analysis with significantly enhanced statistical power. In this study, we explored the causal connection between the gut microbiota and various immune-related vasculitis conditions through an extensive two-sample MR analysis involving four distinct vasculitis types (GCA, BD, KD, and GPA). Utilizing a bidirectional MR approach, we sought to investigate the potential causal impact of the gut microbiota on the risk of immune-related vasculitis and simultaneously assess whether genetic predisposition to immune-related vasculitis risk causally influences the composition of the gut microbiota. These analyses aimed to elucidate the role of the gut microbiota in the development of immune-related vasculitis, contributing to the eventual formulation of novel treatment strategies. 2. Methods 2.1. Study design We conducted a two-sample MR analysis to explore the potential causal link between immune-related vasculitis and the gut microbiome. The study's workflow is depicted in Fig. 1 . To ensure the validity of the IVs, we adhered to the three foundational assumptions of the MR design: (I) the genetic variation used as an IV must exhibit a significant association with the exposure(s); (ii) the genetic variation must be independent of confounding factors; and (iii) the variation must solely relate to the outcome(s) through the exposure(s) [ 34 ]. Initial emphasis was placed on establishing causation, treating the gut microbiome as the exposure and immune-related vasculitis as the outcome. Subsequently, we analyzed the reverse causal direction, considering immune-related vasculitis as the exposure and the gut microbiome as the outcome (refer to Fig. 1 for detailed insights). 2.2. GWAS data sources The international consortium MiBioGen conducted a comprehensive genome-wide meta-analysis of gut microbiota-related GWAS data involving 18,340 participants from 24 cohorts representing diverse ethnicities [ 33 ]. The resulting dataset includes 211 GWAS summary statistics for bacterial taxa, covering 9 phyla, 16 classes, 20 orders, 35 families (including 3 with unknown classifications), and 131 genera (with 12 having unknown taxonomies). The detailed taxonomic categorizations are presented in Supplementary Table 1. In addition, MR analyses incorporated the remaining bacterial taxa from five alternative phyla and their subcategories, enhancing potential evidence for causality. After excluding 15 families and genera with unknown taxonomic classifications, a total of 196 taxa across diverse hierarchical levels were selected as the focal exposure of interest in our investigation. Additionally, GCA and BD summary statistics were derived from FinnGen Release 9 [ 35 ] using the phenocodes "M13_GIANTCELL" and "M13_BEHCET". The GCA dataset included 366,529 samples, comprising 996 cases and 365,533 controls, while the BD dataset included 365,618 samples, comprising 85 cases and 365,533 controls. Summary data for KD were acquired from the IEU OpenGWAS database available at https://gwas.mrcieu.ac.uk/ . The KD dataset included 6,190 samples, comprising 119 cases and 6,071 controls and encompassing a total of 152,542 genotyped SNPs [ 36 ]. Summary data for GPA were acquired from the publicly available GWAS catalog ( https://www.ebi.ac.uk/gwas/ ). The GPA dataset included 456,348 samples, comprising 135 cases and 456,213 controls [ 37 ]. 2.3. IV selection To identify potential associations between exposure and outcome, we employed stringent criteria for selecting SNPs as IVs. When considering the gut microbiome as the exposure factor, a p value < 1 × 10 − 5 was considered to indicate statistical significance. Additionally, the linkage disequilibrium threshold was established at r² < 0.01, and the search distance for linkage disequilibrium r² values was limited to 500 kb. To assess the potential causal impact of immune-related vasculitis on the bacterial genera, we conducted a reverse MR analysis. In the case of immune-related vasculitis being the exposure, the significance level for IVs was set at a p value < 1 × 10 − 4 . The linkage disequilibrium threshold and clumping window were defined as r² < 0.01 and 250 kb, respectively. To assess potential weak IV bias, the F-statistic of the IVs was computed, with a threshold F-statistic < 10 indicating weak IV bias [ 38 ]. IVs failing to meet this criterion (F-statistic < 10) were excluded to ensure robustness. The dataset was further refined by removing ambiguous and palindromic SNPs. 2.4. MR analyses MR analyses were performed employing various methods, including inverse variance-weighted (IVW), weighted median, MR–Egger, and maximum likelihood approaches, to discern associations between the gut microbiome and three subtypes of immune-related vasculitis. The IVW approach, with an assumption of the validity of all SNPs as variables, served as the primary method. The weighted median approach provides consistent estimates under the assumption that more than half of the weights originate from valid SNPs [ 39 ]. MR–Egger analysis, which is capable of calibrating for pleiotropy, facilitates causal inference even in the presence of pleiotropic genetic variants [ 40 ]. The maximum likelihood-based approach was employed to ensure appropriate confidence interval (CI) estimation in the presence of weak IVs. Interpretative guidelines for these methods can be found elsewhere [ 41 ]. In sensitivity analyses, heterogeneity was assessed to gauge the compatibility of instrumental variables. Cochran’s Q statistics, implemented through the IVW and MR–Egger methods, were used to test for heterogeneity, and consideration of its effect was warranted if it was present among IVs (p < 0.05) [ 42 ]. The identification of horizontal pleiotropy, signaling that IVs are associated with outcomes through mechanisms other than causal effects and potentially leading to false-positive results (p < 0.05) [ 43 ], was considered crucial. Direct association testing between selected IVs and outcomes involved horizontal pleiotropy assessment using MR pleiotropy residual sum and outlier (MR-PRESSO). Leave-one-out analysis was also conducted to determine whether a single SNP disproportionately influenced the causal effect of exposure on outcomes. This approach involved iteratively omitting each SNP from IVs during IVW testing and assessing potential outliers using the TwoSampleMR package (version 0.5.7) [ 44 ]. False discovery rate (FDR) correction was applied via the q value procedure, with the threshold set as a q value < 0.1 [ 45 ]. Taxa associations between the gut microbiota and immune-related vasculitis were considered suggestive if p < 0.05 but q ≥ 0.1. Specific FDR thresholds were established for various taxonomic levels: phyla (9), classes (16), orders (20), families (32), and genera (119). All analyses were conducted using R software (version 4.3.1). Venn diagram analysis was carried out using SUMO online software ( https://angiogenesis.dkfz.de/oncoexpress/software/sumo/ ). 3. Results 3.1. SNP selection Following our screening criteria, 27,548 SNPs were identified as IVs through a large-scale GWAS. A comprehensive set of 196 taxa spanning five biological classifications (phylum, class, order, family, and genus) was chosen as the exposure conditions. In the context of immune-related vasculitis as the exposure, 10 SNPs for BD, 89 SNPs for GCA, 35 SNPs for KD, and 77 SNPs for GPA were selected as IVs. All IVs exhibited F statistics well above 10 (refer to Supplementary Table 2), indicating an absence of weak instrument bias. In investigating the causal relationship between the gut microbiota and immune-related vasculitis, the primary interpretation relied on the IVW results, complemented by findings from four additional tests: detailed results from the IVW, weighted median, MR–Egger, and maximum likelihood approaches, along with outcomes regarding heterogeneity and pleiotropy, are available in Supplementary Tables 3–10. In the case of heterogeneity or pleiotropy (p < 0.05), any IV displaying such characteristics was excluded (see Supplementary Tables 11 and 12). 3.2. Positive causal effects of the gut microbiota on immune-related vasculitis, determined after FDR correction After FDR correction, the results of IVW analyses demonstrated that the abundance of the genus Ruminococcaceae NK4A214 group (OR = 0.734, 95% CI = 0.614–0.877, p = 0.001, q = 0.081) was negatively associated with the risk of GCA. Additionally, the abundances of the families Clostridiaceae 1 (OR = 1.699, 95% CI = 1.314–2.196, p = 5.33E-05, q = 0.001) and Actinomycetaceae (OR = 2.333, 95% CI = 1.899–2.867, p = 7.68E-16, q = 1.23E-14) were positively associated with the risk of GPA. Moreover, the abundances of the class Lentisphaeria (OR = 0.649, 95% CI = 0.545–0.773, p = 1.28E-06, q = 4.08E-06), class Melainabacteria (OR = 0.675, 95% CI = 0.567–0.804, p = 1.00E-05, q = 2.67E-05), class Negativicutes (OR = 0.696, 95% CI = 0.51–0.951, p = 0.023, q = 0.04), family Lachnospiraceae (OR = 0.653, 95% CI = 0.515–0.828, p = 4.30E-04, q = 0.003), family Porphyromonadaceae (OR = 0.503, 95% CI = 0.309–0.818, p = 5.650E-03, q = 0.03), family Ruminococcaceae (OR = 0.442, 95% CI = 0.233–0.838, p = 0.012, q = 0.06), and family Streptococcaceae (OR = 0.4, 95% CI = 0.23–0.695, p = 0.001, q = 0.007) were negatively associated with the risk of GPA (Table 1 ). In reverse MR analysis, BD was found to be positively associated with the risk of the family Streptococcaceae (OR = 1.021, 95% CI = 0.996–1.022, p = 0.002, q = 0.065) (Table 2 ). Table 1 Positive MR results of the causal relationship between gut microbiota and GPA and GCA risk after FDR correction. Taxa Gut microbiota (exposure) Outcome Methods SNPs (n) OR (95% CI) P -value q-value Test of heterogeneity Test of pleiotropy Cochran’s Q P -value Egger intercept SE P -value Genus Ruminococcaceae NK4A214 group GCA MR Egger 105 0.648(0.363–1.155) 0.144 1.000 96.834 0.652 0.008 0.018 0.659 Weighted median 105 0.726(0.56–0.941) 0.016 0.927 IVW 105 0.734(0.614–0.877) 0.001 0.081 97.031 0.673 Simple mode 105 0.784(0.39–1.579) 0.497 1.000 Weighted mode 105 0.537(0.286–1.01) 0.057 1.000 Class Negativicutes GPA MR Egger 310 0.439(0.16–1.205) 0.111 0.296 288.058 0.787 0.028 0.03 0.348 Weighted median 310 1.13(0.717–1.781) 0.599 0.599 IVW 310 0.696(0.51–0.951) 0.023 0.040 288.942 0.788 Simple mode 310 3.006(0.602–15.005) 0.181 0.321 Weighted mode 310 2.904(0.603–13.98) 0.185 0.657 Class Melainabacteria GPA MR Egger 327 0.854(0.423–1.725) 0.66 0.094 243.851 1 -0.022 0.03 0.499 Weighted median 327 0.644(0.506–0.819) 3.35E-04 8.94E-04 IVW 327 0.675(0.567–0.804) 1.00E-05 2.67E-05 244.31 1 Simple mode 327 1.467(0.599–3.594) 0.403 0.500 Weighted mode 327 1.435(0.608–3.389) 0.411 1.000 Class Lentisphaeria GPA MR Egger 300 1.465(0.625–3.436) 0.38 0.608 331.138 0.091 -0.086 0.05 0.056 Weighted median 300 0.586(0.459–0.747) 1.68E-05 5.38E-05 IVW 300 0.649(0.545–0.773) 1.28E-06 4.08E-06 335.213 0.073 Simple mode 300 0.164(0.053–0.512) 2.03E-03 0.008 Weighted mode 300 0.172(0.065–0.451) 4.06E-04 0.002 Family Streptococcaceae GPA MR Egger 89 0.139(0.036–0.542) 5.53E-03 0.059 101.063 0.144 0.081 0.05 0.1 Weighted median 89 0.428(0.197–0.926) 0.031 0.125 IVW 89 0.4(0.23–0.695) 0.001 0.007 104.275 0.114 Simple mode 89 0.377(0.05–2.845) 0.347 0.925 Weighted mode 89 0.336(0.063–1.799) 0.206 0.732 Family Porphyromonadaceae GPA MR Egger 112 0.56(0.146–2.151) 0.4 0.854 107.26 0.556 -0.007 0.04 0.867 Weighted median 112 0.613(0.291–1.293) 0.199 0.454 IVW 112 0.503(0.309–0.818) 5.65E-03 0.030 107.288 0.582 Simple mode 112 0.579(0.089–3.763) 0.568 1.000 Weighted mode 112 0.695(0.099–4.902) 0.716 1.000 Family Lachnospiraceae GPA MR Egger 533 1.076(0.474–2.446) 0.861 0.888 287.359 1 -0.029 0.02 0.213 Weighted median 533 0.515(0.372–0.715) 6.98E-05 4.46E-04 IVW 533 0.653(0.515–0.828) 4.30E-04 0.003 288.917 1 Simple mode 533 0.427(0.117–1.554) 0.197 0.631 Weighted mode 533 0.441(0.123–1.59) 0.212 0.677 Family Clostridiaceae 1 GPA MR Egger 344 0.869(0.373–2.029) 0.746 0.880 320.495 0.792 0.045 0.03 0.105 Weighted median 344 1.807(1.243–2.626) 0.002 0.010 IVW 344 1.699(1.314–2.196) 5.33E-05 0.001 323.139 0.773 Simple mode 344 1.472(0.399–5.44) 0.562 1.000 Weighted mode 344 1.472(0.358–6.053) 0.592 0.997 Family Actinomycetaceae GPA MR Egger 332 2.262(0.992–5.157) 0.053 0.243 369.839 0.064 0.003 0.03 0.939 Weighted median 332 2.867(2.125–3.87) 5.71E-12 1.83E-10 IVW 332 2.333(1.899–2.867) 7.68E-16 1.23E-14 369.846 0.069 Simple mode 332 11.45(3.591–36.505) 4.77E-05 0.002 Weighted mode 332 10.909(3.77-31.567) 1.41E-05 4.52E-04 Family Ruminococcaceae GPA MR Egger 75 0.645(0.105–3.949) 0.637 0.927 84.008 0.178 -0.025 0.057 0.663 Weighted median 75 0.599(0.232–1.547) 0.29 0.580 IVW 75 0.442(0.233–0.838) 0.012 0.060 84.229 0.195 Simple mode 75 1.043(0.101–10.747) 0.972 0.972 Weighted mode 75 0.969(0.149–6.301) 0.974 1.000 GCA: giant cell arteritis; GPA: granulomatosis with polyangiitis; FDR: False discovery rate; MR: mendelian randomization; IVW: inverse variance weighted; SNPs: single nucleotide polymorphisms; OR: odds ratio; CI: confidence intervals; SE: standard error. Table 2 Positive MR results of the causal relationship between gut microbiota and BD risk after FDR correction. Exposure Taxa Gut microbiota (outcome) Methods SNPs (n) OR (95% CI) P -value Test of heterogeneity Test of pleiotropy Cochran’s Q P -value Egger intercept SE P -value BD Family Streptococcaceae MR Egger 10 1.024(0.933–1.094) 0.569 5.046 0.753 -0.002 0.026 0.935 Weighted median 10 1.022(0.992–1.028) 0.015 IVW 10 1.021(0.996–1.022) 0.002 5.053 0.830 Simple mode 10 1.029(0.984–1.042) 0.085 Weighted mode 10 1.025(0.983–1.04) 0.112 BD: Behcet's disease; FDR: False discovery rate; MR: mendelian randomization; IVW: inverse variance weighted; SNPs: single nucleotide polymorphisms; OR: odds ratio; CI: confidence intervals; SE: standard error. 3.3. Potential causal effects of the gut microbiota on immune-related vasculitis 3.3.1. BD Forward MR analysis. The results of IVW analyses demonstrated that the abundances of the class Melainabacteria (odds ratio (OR) = 1.851, 95% CI = 0.838–2.037, p = 0.007), class Gammaproteobacteria (OR = 1.949, 95% CI = 0.695–2.571, p = 0.046), family Rhodospirillaceae (OR = 1.744, 95% CI = 0.764–2.123, p = 0.033), genus Ruminococcaceae UCG011 (OR = 1.533, 95% CI = 0.837–1.733, p = 0.021), and genus Odoribacter (OR = 2.215, 95% CI = 0.764–2.613, p = 0.011) were potentially positively associated with the risk of BD (Supplementary Table 3, Fig. 2 ). Reverse MR analysis. When considering BD as an exposure and the gut microbiota as an outcome, the results of IVW analyses demonstrated that BD was potentially positively associated with the risk of the class Bacilli (OR = 1.018, 95% CI = 0.995–1.021, p = 0.008), order Lactobacillales (OR = 1.018, 95% CI = 0.995–1.021, p = 0.007), genus Holdemanella (OR = 1.022, 95% CI = 0.99–1.029, p = 0.028), and genus Streptococcus (OR = 1.018, 95% CI = 0.995–1.021, p = 0.007) (Supplementary Table 4, Fig. 3 ). Moreover, BD was potentially negatively associated with the risk of the phylum Verrucomicrobia (OR = 0.984, 95% CI = 0.978–1.008, p = 0.043), the class Verrucomicrobiae (OR = 0.984, 95% CI = 0.978–1.009, p = 0.045), the order Verrucomicrobiales (OR = 0.984, 95% CI = 0.978–1.009, p = 0.045), the family Verrucomicrobiaceae (OR = 0.984, 95% CI = 0.978–1.009, p = 0.045), the genus Akkermansia (OR = 0.984, 95% CI = 0.978–1.009, p = 0.042), the genus Ruminiclostridium 6 (OR = 0.984, 95% CI = 0.979–1.008, p = 0.029), and the genus Coprococcus 3 (OR = 0.985, 95% CI = 0.98–1.007, p = 0.028) (Supplementary Table 4, Fig. 3 ). 3.3.2. GCA Forward MR analysis. The results of IVW analyses demonstrated that the genera Lachnospiraceae UCG004 (OR = 1.214, 95% CI = 1.015–1.453, p = 0.034), Ruminococcus 2 (OR = 1.199, 95% CI = 1.003–1.434, p = 0.047), Ruminococcus gnavus group (OR = 1.142, 95% CI = 1.018–1.28, p = 0.023), Holdemania (OR = 1.225, 95% CI = 1.044–1.437, p = 0.013), Flavonifractor (OR = 1.225, 95% CI = 1.048–1.432, p = 0.011), Dialister (OR = 1.2, 95% CI = 1.029-1.4, p = 0.02), Bilophila (OR = 1.226, 95% CI = 1.043–1.44, p = 0.013), and Eubacterium nodatum group (OR = 1.148, 95% CI = 1.048–1.257, p = 0.003) were potentially positively associated with the risk of GCA (Supplementary Table 5, Fig. 2 ). Moreover, the phylum Verrucomicrobia (OR = 0.796, 95% CI = 0.673–0.943, p = 0.008), family Porphyromonadaceae (OR = 0.833, 95% CI = 0.696–0.998, p = 0.048), genus Veillonella (OR = 0.872, 95% CI = 0.768–0.991, p = 0.036), genus Erysipelatoclostridium (OR = 0.835, 95% CI = 0.72–0.968, p = 0.017), and genus Adlercreutzia (OR = 0.858, 95% CI = 0.745–0.988, p = 0.034) were potentially negatively associated with the risk of GCA (Supplementary Table 5, Fig. 2 ). Reverse MR analysis. When GCA was considered an exposure and the gut microbiota was considered an outcome, the results of IVW analyses demonstrated that GCA was potentially positively associated with the risk of the genus Erysipelotrichaceae UCG003 (OR = 1.079, 95% CI = 0.987–1.083, p = 0.001) (Supplementary Table 6, Fig. 3 ). Moreover, GCA was potentially negatively associated with the risk of the family Victivallaceae (OR = 0.974, 95% CI = 0.964–1.014, p = 0.044), genus Alistipes (OR = 0.985, 95% CI = 0.982–1.005, p = 0.011), and genus Ruminococcaceae UCG010 (OR = 0.986, 95% CI = 0.98–1.008, p = 0.046) (Supplementary Table 6, Fig. 3 ). 3.3.3. KD Forward MR analysis. The results of IVW analyses demonstrated that the phylum Lentisphaerae (OR = 2.8, 95% CI = 0.675–3.623, p = 0.016), genus Lachnospira (OR = 5.506, 95% CI = 0.522–8.428, p = 0.016), and genus Victivallis (OR = 2.368, 95% CI = 0.785–2.692, p = 0.006) were potentially positively associated with the risk of KD (Supplementary Table 7, Fig. 2 ). Moreover, the family Prevotellaceae (OR = 0.094, 95% CI = 0.053–2.416, p = 0.015), genus Ruminiclostridium 9 (OR = 0.217, 95% CI = 0.131–2.023, p = 0.029), genus Lactobacillus (OR = 0.317, 95% CI = 0.205–1.798, p = 0.038), and genus Bifidobacterium (OR = 0.196, 95% CI = 0.098–2.473, p = 0.048) were potentially negatively associated with the risk of KD (Supplementary Table 7, Fig. 2 ). Reverse MR analysis. When KD was considered an exposure and the gut microbiota was considered an outcome, the results of IVW analyses demonstrated that KD was potentially positively associated with the risk of the genus Eubacterium oxidoreducens group (OR = 1.013, 95% CI = 0.993–1.018, p = 0.041) (Supplementary Table 8, Fig. 3 ). Moreover, KD was potential negatively associated with the risk of the order Bacillales (OR = 0.979, 95% CI = 0.976–1.005, p = 0.005), order Actinomycetales (OR = 0.988, 95% CI = 0.985–1.004, p = 0.01), family Actinomycetaceae (OR = 0.988, 95% CI = 0.985–1.004, p = 0.01), family Ruminococcaceae (OR = 0.994, 95% CI = 0.991–1.003, p = 0.044), family Defluviitaleaceae (OR = 0.987, 95% CI = 0.985–1.004, p = 0.006), family Rikenellaceae (OR = 0.994, 95% CI = 0.991–1.004, p = 0.048), genus Alistipes (OR = 0.993, 95% CI = 0.99–1.003, p = 0.02), genus Eubacterium eligens group (OR = 0.991, 95% CI = 0.989–1.003, p = 0.017), genus Ruminococcus torques group (OR = 0.993, 95% CI = 0.991–1.003, p = 0.018), genus Ruminococcus 2 (OR = 0.993, 95% CI = 0.991–1.004, p = 0.048), genus Streptococcus (OR = 0.993, 95% CI = 0.991–1.004, p = 0.044), genus Actinomyces (OR = 0.988, 95% CI = 0.985–1.004, p = 0.014), genus Defluviitaleaceae UCG011 (OR = 0.987, 95% CI = 0.985–1.003, p = 0.005), genus Butyricimonas (OR = 0.991, 95% CI = 0.988–1.005, p = 0.037), and genus Romboutsia (OR = 0.992, 95% CI = 0.99–1.004, p = 0.03) (Supplementary Table 8, Fig. 3 ). 3.3.4. GPA Forward MR analysis. The results of IVW analyses demonstrated that the genera Turicibacter (OR = 1.465, 95% CI = 1.001–2.143, p = 0.049), Paraprevotella (OR = 1.513, 95% CI = 1.049–2.182, p = 0.027), Parabacteroides (OR = 1.899, 95% CI = 1.17–3.082, p = 0.009), Christensenellaceae R.7 group (OR = 1.688, 95% CI = 1.058–2.693, p = 0.029), Butyricimonas (OR = 1.58, 95% CI = 1.077–2.316, p = 0.019), and Anaerotruncus (OR = 1.684, 95% CI = 1.019–2.784, p = 0.042) were potentially positively associated with the risk of GPA (Supplementary Table 9, Fig. 2 ). Moreover, the order Gastranaerophilales (OR = 0.679, 95% CI = 0.477–0.966, p = 0.031), family Family XIII (OR = 0.602, 95% CI = 0.369–0.982, p = 0.042), genus Lachnospira (OR = 0.544, 95% CI = 0.342–0.865, p = 0.01), genus Blautia (OR = 0.616, 95% CI = 0.381–0.997, p = 0.048), and genus Bacteroides (OR = 0.583, 95% CI = 0.349–0.975, p = 0.04) were potentially negatively associated with the risk of GPA (Supplementary Table 9, Fig. 2 ). Reverse MR analysis. When GPA was considered an exposure and the gut microbiota was considered an outcome, the results of IVW analyses demonstrated that GPA was potentially positively associated with the risk of the genera Holdemanella (OR = 1.01, 95% CI = 0.996–1.012, p = 0.02) and Barnesiella (OR = 1.006, 95% CI = 0.997–1.008, p = 0.022) (Supplementary Table 10, Fig. 3 ). Moreover, GPA was potentially negatively associated with the risk of the genera Eubacterium oxidoreducens group (OR = 0.99, 95% CI = 0.987–1.004, p = 0.023), Ruminococcaceae UCG009 (OR = 0.992, 95% CI = 0.989–1.004, p = 0.037), Lachnospiraceae UCG004 (OR = 0.993, 95% CI = 0.992–1.002, p = 0.008), Roseburia (OR = 0.995, 95% CI = 0.993–1.002, p = 0.037), and Veillonella (OR = 0.991, 95% CI = 0.99–1.003, p = 0.008) (Supplementary Table 10, Fig. 3 ). 3.4. Potential causal interactions between the gut microbiota and immune-related vasculitis Taken together, the positive association results from IVW analysis (p < 0.05) revealed numerous shared gut microbiota constituents associated with immune-related vasculitis. Regardless of the role of the gut microbiota as an exposure or outcome, Venn diagrams illustrated intricate intersections among immune-related vasculitis subtypes (Fig. 4 ). Consequently, an interaction diagram depicting the interplay of the gut microbiota in immune-related vasculitis was constructed (Fig. 5 ). 3.4.1. The gut microbiota positively interacts with at least three types of immune-related vasculitis The gut microbiota, particularly Ruminococcaceae/Ruminococcus , exhibits prominent interactions with immune-related vasculitis. Notably, the abundance of the family Ruminococcaceae demonstrated a negative association with the risk of GPA, and KD exhibited a negative association with the risk of the family Ruminococcaceae . Additionally, the abundances of specific genera within Ruminococcaceae , such as Ruminococcaceae UCG011 , Ruminococcus gnavus group and Ruminococcus 2 , exhibited positive associations with the risk of BD and GCA. Conversely, the abundance of Ruminococcaceae NK4A214 group exhibited a negative association with the risk of GCA. Furthermore, KD, GPA and GCA were negatively associated with the risk of the genera Ruminococcus 2 , Ruminococcus torques group , Ruminococcaceae UCG009 , and Ruminococcaceae UCG010 . The second set of gut microbiota constituents interacting with immune-related vasculitis included Eubacterium , Lachnospira/Lachnospiraceae , and Holdemanella/Holdemania . Specifically, the abundance of the genus Eubacterium nodatum group exhibited a positive association with the risk of GCA, while KD demonstrated a positive association with the risk of the genus Eubacterium oxidoreducens group . Conversely, GPA was negatively associated with the risk of the genus Eubacterium oxidoreducens group . The abundances of the family Lachnospiraceae and genus Lachnospira are negatively associated with the risk of GPA. The abundances of the genera Lachnospira and Lachnospiraceae UCG004 demonstrated positive associations with the risk of KD and GCA, respectively. Conversely, GPA was negatively associated with the risk of the genus Lachnospiraceae UCG004 . BD and GPA both showed positive associations with the risk of the genus Holdemanella . Additionally, the abundance of the genus Holdemania was positively associated with the risk of GCA. 3.4.2. The interaction of the gut microbiota with two types of immune-related vasculitis BD and GCA. BD was negatively associated with the risk of the phylum Verrucomicrobia, family Verrucomicrobiaceae, class Verrucomicrobiae, and order Verrucomicrobiales. Concurrently, the abundance of the phylum Verrucomicrobia demonstrated a negative association with the risk of GCA. GCA and GPA. GPA was negatively associated with the risk of the genus Veillonella , and the abundance of the genus Veillonella was negatively associated with the risk of GCA. Simultaneously, the abundance of the family Porphyromonadaceae showed a negative association with the risk of GCA and GPA. BD and GPA. The abundance of the class Melainabacteria demonstrated a positive association with the risk of BD; conversely, it exhibited a negative association with the risk of GPA. Similarly, the abundance of the family Streptococcaceae exhibited a negative association with the risk of GPA; conversely, BD showed a positive association with the risk of the family Streptococcaceae . GCA and KD. GCA and KD were both negatively associated with the risk of the genus Alistipes , and GCA was also negatively associated with the risk of the family Victivallaceae . Conversely, the abundance of the genus Victivallis was positively associated with the risk of KD. KD and GPA. Both KD and GPA were negatively associated with the risk of the genus Eubacterium eligens group , with KD also exhibiting negative associations with the risk of the order Actinomycetales , family Actinomycetaceae , genus Actinomyces and genus Butyricimonas . In contrast, the abundances of the family Actinomycetaceae and genus Butyricimonas were positively associated with the risk of GPA. BD and KD. BD was positively associated with the risk of the order Lactobacillales and the genus Streptococcus . Conversely, KD was negatively associated with the risk of the genus Streptococcus . BD is also negatively associated with the risk of the genus Ruminiclostridium 6 . The abundances of the genera Lactobacillus and Ruminiclostridium 9 were negatively associated with the risk of KD. 4. Discussion This MR study represents the first examination of the potential causal association between the gut microbiota and immune-related vasculitis, which we expect will serve as a foundation for future longitudinal investigations of alterations in the microbiome preceding the onset of immune-related vasculitis. Unexpectedly, genetic predispositions to colonization with the class Melainabacteria , class Lentisphaeria , and family Actinomycetaceae demonstrated causal links with GPA. Additionally, we identified specific gut microbiota constituents that could serve as potential risk factors for immune-related vasculitis. These findings will support public health interventions aimed at mitigating the risk of immune-related vasculitis. GCA, characterized by granulomatous inflammation in large and medium-sized vessels, primarily affects elderly patients [ 3 ]. No specific investigation has been dedicated to exploring the gut microbiome in GCA patients. However, studies focusing on the microbiome in the blood and aorta have highlighted distinctions in GCA patients. These distinctions include a decrease in Actinobacteria abundance and an increase in Proteobacteria abundance and minimal Bacteroidetes abundance compared to those in individuals with noninflammatory thoracic aortic aneurysms. Moreover, in contrast to patients with non-GCA temporal arteritis, GCA patients had variable proportions of Proteobacteria , Bifidobacterium , Parasutterella , and Granulicatella . Notably, there was variation in the abundance of Rhodococcus , an unidentified member of the family Cytophagaceae , in blood samples. Additionally, GCA patients exhibit dissimilarities in microbiome composition between the temporal artery and thoracic artery [ 12 – 15 ]. Our study first revealed a negative association between the abundance of the genus Ruminococcaceae NK4A214 group and the risk of GCA. Ruminococcaceae NK4A214 group is linked to fiber degradation [ 46 ]. Additionally, research has indicated its role in advancing type 2 diabetes in a Mexican cohort [ 47 ]. Moreover, the abundance of Ruminococcaceae NK4A214 group effectively differentiated between chronic kidney disease patients and healthy controls [ 48 ]. Moreover, our study identified a complex interaction network between immune-related vasculitis and Ruminococcaceae spp., suggesting the potential relevance of this genus to the onset and progression of immune-related vasculitis. Additionally, we identified other gut microbiota constituents, including the genera Veillonella , Ruminococcus 2 , Flavonifractor , and Ruminococcus gnavus , as potential risk factors for GCA. BD is an uncommon form of vasculitis, its etiology remains elusive, and it involves multiple organs. The disease is characterized by mucocutaneous symptoms such as oral and genital aphthosis and aseptic folliculitis. Additionally, patients may experience ocular complications such as uveitis, vascular issues leading to thrombosis, and further manifestations in the articular, gastrointestinal, and neurological systems [ 4 ]. Research on the role of the gut microbiome in BD patients surpasses that for other vasculitis types. 16S rRNA sequencing revealed that BD patients exhibit gut microbiota dysbiosis, which impacts intestinal immune function and influences BD progression. These associations were initially highlighted in mouse models. Ongoing clinical trials are exploring microbial therapies for BD, though the efficacy of dietary interventions remains unclear. Cross-sectional analyses revealed distinct gut bacterial profiles in BD patients, which were characterized by elevated abundances of lactic acid and sulfate-producing bacteria but reduced abundances of lactic butyric acid producers and methanogens [ 16 – 24 ]. Notably, research has shown specific microbial shifts, such as increases in Tenericutes abundance and decreases in Deferribacteres and Verrucomicrobia abundance, in BD mice. Additionally, treatments with butyrate or Eubacterium rectale , a butyrate-producing bacterium, mitigated BD symptoms, suggesting therapeutic potential [ 49 ]. According to reverse MR analysis, our study revealed that BD was positively associated with the risk of the family Streptococcaceae . Initially, the role of Streptococcus spp. in BD was investigated, given their prevalence in the oral bacterial community and association with oral diseases. Elevated abundances of distinct streptococcal serotypes have been observed in the oral mucosa of BD patients compared to those of healthy individuals [ 50 , 51 ]. Further substantiating the pathogenic potential of Streptococcus in BD, mouse studies revealed that introducing Streptococcus sanguinis from BD patients led to the manifestation of BD-related symptoms [ 52 ]. Therefore, the aforementioned preliminary findings align with our results. Our study delineated a multifaceted interaction network between Streptococcus spp. and immune-related vasculitis, encompassing BD, KD, and GPA, indicating the potential involvement of these bacteria in the initiation and progression of these conditions. Furthermore, we identified additional gut microbiota constituents, such as the classes Melainabacteria and Gammaproteobacteria , family Rhodospirillaceae , and genera Odoribacter and Ruminococcaceae UCG011 , that may serve as potential risk factors for BD. KD is severe systemic vasculitis frequently associated with coronary artery aneurysms [ 53 ]. The susceptibility of the intestinal microbiome to environmental factors is believed to influence KD development [ 54 , 55 ]. Associations with KD have been identified for Bacteroidetes and Dorea [ 56 ], and Fusobacteria , Shigella , and Streptococcus have also been suggested as potential influencing factors [ 57 ]. While the Ruminococcus abundance increases during the nonacute stages of KD, Streptococcus becomes more enriched in the acute phases [ 58 ]. A comparison of microbial sequences from throat, rectum, and blood samples highlighted similarities between the blood and gut microbiota [ 59 ]. In a murine model of exposure to a Lactobacillus cell wall extract, both bacteria and fungi were demonstrated to influence KD progression and severity [ 60 ]. Antibiotic administration has also been associated with KD development [ 61 ]. After treatment with immunoglobulin/antibiotics, a decrease in the abundances of harmful bacteria and an increase in the abundances of beneficial bacteria have been observed [ 57 ]. This microbial shift could enhance intestinal permeability, allowing intestinal pathogens to induce irregular immune reactions. Our study revealed that the abundance of the genus Lactobacillus was potentially negatively associated with the risk of KD. Moreover, KD was potentially negatively associated with the risk of the family Ruminococcaceae , genus Ruminococcus torques group , genus Ruminococcus 2 , and genus Streptococcus . These findings diverge from those of the aforementioned observational studies, which we primarily attribute to the limited sample size of KD patients in our MR analysis. Consequently, the conclusions drawn regarding the causal association between KD and the gut microbiota should be interpreted as indicative rather than definitive, warranting further investigation in subsequent research. GPA is a rare necrotizing vasculitis primarily affecting the upper and lower respiratory tracts and the renal system and involving small to medium-sized vessels [ 9 ]. Recent research has elucidated the association between GPA and the nasal microbiome. Rhee et al. observed fluctuations in the nasal microbiota of GPA patients, particularly in the Corynebacterium -to- Staphylococcus ratio, with distinct changes preceding disease relapses [ 62 ]. Using deep sequencing, another group discerned a microbial imbalance in GPA patients at the bacterial and fungal tiers, with immunosuppressive therapy associated with a more normalized profile [ 63 ]. Wagner et al. identified enrichment of Staphylococcus aureus in patients with active GPA, contrasting with the prevalence of Staphylococcus epidermidis in patients with inactive GPA [ 64 ]. Furthermore, Lamprecht et al. observed reduced microbiome diversity in GPA patients and revealed enhanced colonization of Staphylococcus aureus , along with pathogens such as Haemophilus influenzae and rhinoviruses, emphasizing the microbial dysbiosis [ 65 ]. Together, these findings underscore the nuanced interplay between GPA and the nasal microbiome, emphasizing the potential roles of specific bacterial genera. Further investigations are essential to clarify the underlying mechanisms and possible therapeutic approaches. Niccolai et al. investigated the gut microbiota in eosinophilic granulomatosis with polyangiitis patients and identified an increase in the abundance of potential pathobionts, specifically Enterobacteriaceae and Streptococcaceae , during active disease [ 66 ]. Concurrently, Yu et al. noted increased Actinomyces and Streptococcus abundances and reduced abundances of SCFA-producing taxa in microscopic polyangiitis patients [ 67 ]. To date, no published study has explored the association between GPA and the gut microbiota. The present MR investigation was conducted based on the most recent GWAS datasets for GPA. The GWAS dataset for GPA included 135 cases and 456,213 controls from the publicly available GWAS catalog. Consequently, our study revealed that the abundances of the families Clostridiaceae 1 and Actinomycetaceae were positively associated with the risk of GPA. Moreover, the abundances of the classes Lentisphaeria , Melainabacteria , and Negativicutes and the families Lachnospiraceae , Porphyromonadaceae , Ruminococcaceae , and Streptococcaceae were negatively associated with the risk of GPA. This study represents the first exploration of the causal association between GPA and the gut microbiota on the bases of a comprehensive GWAS sample, yielding robust and credible findings. Through this research, we identified a shared gut microbiota signature, notably involving the Streptococcaceae family, between GPA and other AAVs. The relationship between the gut microbiota and immune-related vasculitis highlights the potential regulatory role of the gut microbiome in these diseases. The overlap of gut microbiota patterns among diverse vasculitides indicates that shared underlying mechanisms are influenced by the gut microbial composition, suggesting potential therapeutic targets applicable to multiple vasculitis conditions. Specifically, the family Ruminococcaceae exhibits distinct associations across diseases (e.g., negative association with GPA but positive association with BD and GCA), underscoring the importance of a detailed understanding of microbial taxa rather than generalized interpretations. Associations of Eubacterium and Lachnospiraceae emphasize their relevance in vasculitis pathways (e.g., Eubacterium nodatum group with GCA), with certain genera playing possible proinflammatory roles. Conversely, the negative associations of Verrucomicrobia and Veillonella with GCA suggest potential protective roles or altered states in disease contexts. The bidirectional relationships within the microbial ecosystem, exemplified by Actinomycetales and Actinomycetaceae (which are negatively associated with KD and GPA but positively associated with other diseases), emphasize their context-specific roles in different diseases. Moreover, the positive associations of BD with Lactobacillales and Streptococcus contrast with their negative associations with KD, emphasizing the importance of considering broader microbial interactions. In summary, while specific microbial patterns correlate with distinct diseases, necessitating further exploration into their causative, functional, and therapeutic implications in vasculitis. The current study has several distinct strengths. First, this study provides the first application of a 2-sample bidirectional MR approach, probing the causal ties between the gut microbiota and immune-related vasculitis subtypes, namely, GCA, BD, KD, and GPA. This methodology diminishes issues such as reverse causation and confounding effects, which are often prevalent in observational studies. To enhance the robustness of our findings, we integrated multiple MR framework strategies, ensuring consistency in the results both pre- and postoutlier adjustments while also minimizing variability. For comprehensive genetic insights, we utilized an expansive GWAS dataset at the summary level. The observed disparities between exposure and outcome data further validate our conclusions. Nevertheless, several limitations were evident in this research. Initially, the limited number of SNPs employed as IVs reduced overall statistical robustness. However, given that our F-statistics consistently surpassed 10, the risk of significant instrumental bias in our conclusions remained minimal. underscoring the necessity for larger sample sizes in subsequent MR investigations. Second, the predominant European ancestry of our participants prohibits direct generalization to diverse racial or ethnic populations, emphasizing the need for replicative studies. Finally, our analysis focused solely on bacterial taxa at the genus level, not achieving more granular classifications such as species or strains. Utilizing advanced shotgun metagenomic techniques in microbiota GWASs could increase the precision of the results. 5. Conclusions In summary, our results substantiate the causal roles of the class Melainabacteria , Lentisphaeria and family Actinomycetaceae in GPA. Moreover, we identified distinct gut microbiota elements that might act as potential triggers for immune-related vasculitis. This research offers fresh perspectives on the mechanisms underlying the progression of gut microbiota-associated immune-related vasculitis. Declarations Competing interests The authors declare no conflict of interest. Funding This work was supported by funding from STI2030-Major Projects (2021ZD0200600, 2021ZD0200603), the National Key Research and Development Program (2022YFC2009600) (2022YFC2009602), the “Beijing Major Epidemic Prevention and Control Key Specialty Construction Project” (2022). Authors' contributions Yuan H, Zeng X, and Chen S conceptualized and designed the study; Chen S retrieved the data; Chen S analyzed, interpreted, and drafted the article; Nie R, Wang C, Luan H, Xu M, and Gui Y revised the article; Chen S, Nie R, and Wang C generated the graphs and tables. All the authors approved the final version for submission. Data availability This study utilized publicly available datasets, which were obtained from the MiBioGen database (https://mibiogen.gcc.rug.nl/), the GWAS catalog (https://www.ebi.ac.uk/gwas/), the IEU OpenGWAS database (https://gwas.mrcieu.ac.uk/) and the FinnGen consortium (https://www.finngen.fi/). Acknowledgments We appreciated all the genetics consortiums for making the GWAS summary data publicly available. References Jennette, JC. Overview of the 2012 revised International Chapel Hill Consensus Conference nomenclature of vasculitides. 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Arteriosclerosis, Thrombosis, and Vascular Biology 35:A636. https://doi.org/10.1161/atvb.35.suppl_1.636 (2015) Fukazawa, M, Jr., Fukazawa, M, Nanishi, E et al . Previous antibiotic use and the development of Kawasaki disease: a matched pair case-control study. Pediatr Int 62:1044-1048. https://doi.org/10.1111/ped.14255 (2020) Rhee, RL, Lu, J, Bittinger, K et al . Dynamic Changes in the Nasal Microbiome Associated With Disease Activity in Patients With Granulomatosis With Polyangiitis. Arthritis Rheumatol 73:1703-1712. https://doi.org/10.1002/art.41723 (2021) Rhee, RL, Sreih, AG, Najem, CE et al . Characterisation of the nasal microbiota in granulomatosis with polyangiitis. Ann Rheum Dis 77:1448-1453. https://doi.org/10.1136/annrheumdis-2018-213645 (2018) Wagner, J, Harrison, EM, Martinez Del Pero, M et al . The composition and functional protein subsystems of the human nasal microbiome in granulomatosis with polyangiitis: a pilot study. Microbiome 7:137. https://doi.org/10.1186/s40168-019-0753-z (2019) Lamprecht, P, Fischer, N, Huang, J et al . Changes in the composition of the upper respiratory tract microbial community in granulomatosis with polyangiitis. J Autoimmun 97:29-39. https://doi.org/10.1016/j.jaut.2018.10.005 (2019) Niccolai, E, Bettiol, A, Baldi, S et al . Gut Microbiota and Associated Mucosal Immune Response in Eosinophilic Granulomatosis with Polyangiitis (EGPA). Biomedicines 10:https://doi.org/10.3390/biomedicines10061227 (2022) Yu, B, Jin, L, Chen, Z et al . The gut microbiome in microscopic polyangiitis with kidney involvement: common and unique alterations, clinical association and values for disease diagnosis and outcome prediction. Ann Transl Med 9:1286. https://doi.org/10.21037/atm-21-1315 (2021) Additional Declarations No competing interests reported. Supplementary Files SupplementaryTable1.xlsx SupplementaryTable2.xlsx SupplementaryTable312.xlsx Cite Share Download PDF Status: Published Journal Publication published 13 Aug, 2024 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 03 Jul, 2024 Reviews received at journal 25 Jun, 2024 Reviewers agreed at journal 11 Jun, 2024 Reviewers agreed at journal 18 Apr, 2024 Reviews received at journal 10 Mar, 2024 Reviewers agreed at journal 05 Mar, 2024 Reviewers agreed at journal 04 Mar, 2024 Reviewers invited by journal 09 Feb, 2024 Editor assigned by journal 04 Feb, 2024 Editor invited by journal 23 Jan, 2024 Submission checks completed at journal 23 Jan, 2024 First submitted to journal 17 Jan, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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GCA: giant cell arteritis; KD: Kawasaki disease; GPA: granulomatosis with polyangiitis; N: number of cases; SNPs, single-nucleotide polymorphisms; MR: Mendelian randomization.\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3874319/v1/c62b1b31a4849c40aded6b24.jpg"},{"id":50156281,"identity":"347e91e2-7ed7-418a-aa2e-56c8c5e01652","added_by":"auto","created_at":"2024-01-25 11:48:23","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":3284521,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePositive or potentially positive associations determined from forward MR analysis of the relationship between the gut microbiota and immune-related vasculitis. \u003c/strong\u003eBD: Behcet's disease; GCA: giant cell arteritis; KD: Kawasaki disease; GPA: granulomatosis with polyangiitis; MR: Mendelian randomization; SNPs: single-nucleotide polymorphisms; OR: odds ratio; CI: confidence interval.\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3874319/v1/13d1c0ca5bf8b09a2be41c59.jpg"},{"id":50156283,"identity":"eff8ee9b-5fa5-45c5-81f1-f32f8cf577c4","added_by":"auto","created_at":"2024-01-25 11:48:23","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2842808,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePositive or potentially positive associations determined from reverse MR analysis ofthe relationship between the gut microbiota and immune-related vasculitis. \u003c/strong\u003eBD: Behcet's disease; GCA: giant cell arteritis; KD: Kawasaki disease; GPA: granulomatosis with polyangiitis; MR: Mendelian randomization; SNPs: single-nucleotide polymorphisms; OR: odds ratio; CI: confidence interval.\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3874319/v1/68a1c903e2ed9532ac13b22b.jpg"},{"id":50156279,"identity":"83ca269b-ede4-4d9f-a66b-3c5fcf87a741","added_by":"auto","created_at":"2024-01-25 11:48:23","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1033998,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eVenn diagram of the interaction patterns of the gut microbiota in immune-related vasculitis. \u003c/strong\u003eBD: Behcet's disease; GCA: giant cell arteritis; KD: Kawasaki disease; GPA: granulomatosis with polyangiitis; O: outcome; E: exposure.\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3874319/v1/07ecedd5a0e287c4ce436ce8.jpg"},{"id":50156284,"identity":"4643d991-c2c0-4b4b-bf73-c68a47cd3084","added_by":"auto","created_at":"2024-01-25 11:48:23","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1986862,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eInteraction diagram of the gut microbiota in immune-related vasculitis. \u003c/strong\u003eBD: Behcet's disease; GCA: giant cell arteritis; KD: Kawasaki disease; GPA: granulomatosis with polyangiitis.\u003c/p\u003e","description":"","filename":"Figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3874319/v1/fe2118f8960931bca8bff303.jpg"},{"id":63071316,"identity":"d3060339-be82-4be7-99d7-e6c583c3c240","added_by":"auto","created_at":"2024-08-22 20:06:21","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":12060080,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3874319/v1/c3fba45d-d181-463d-a5ea-04f87eea7201.pdf"},{"id":50156285,"identity":"bb61c5d5-16e8-4986-856b-5fbb32c42b76","added_by":"auto","created_at":"2024-01-25 11:48:23","extension":"xlsx","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":11538,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3874319/v1/299f3b1a704fb736da6aad98.xlsx"},{"id":50156927,"identity":"89c35709-b535-408b-be36-366512441291","added_by":"auto","created_at":"2024-01-25 11:56:23","extension":"xlsx","order_by":10,"title":"","display":"","copyAsset":false,"role":"supplement","size":605995,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3874319/v1/29712105a414d1aca8044cd6.xlsx"},{"id":50156282,"identity":"9f29ccd3-5a98-45db-a050-ae1a38a022d5","added_by":"auto","created_at":"2024-01-25 11:48:23","extension":"xlsx","order_by":11,"title":"","display":"","copyAsset":false,"role":"supplement","size":65386,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable312.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3874319/v1/2be9fb2db10eee8f891919ee.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Causal effects of the gut microbiome on immune-related vasculitis: A two-sample Mendelian randomization study","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eVasculitis is an autoimmune disorder characterized primarily by predominant vascular inflammation and destruction. Our study selectively encompasses specific vasculitis types from the 2012 Chapel Hill Nomenclature [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. We focused solely on studies with accessible genome-wide association study (GWAS) summary data related to giant cell arteritis (GCA), beh\u0026ccedil;et's disease (BD), kawasaki disease (KD), and granulomatosis with polyangiitis (GPA). GCA is a systemic vasculitis distinguished by granulomatous inflammation affecting large and medium-sized arteries, which predominantly occurs in individuals aged 50 years or older, with an incidence rate of approximately 10 cases per 100,000 people [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The manifestation of GCA involves granulomatous inflammation, particularly in the temporal artery, resulting in symptoms such as headache, jaw claudication, scalp discomfort, and increased C-reactive protein levels and erythrocyte sedimentation rate [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. BD is a rare autoimmune disorder that causes inflammation and lesions in various body systems. It involves oral and genital ulcers, eye inflammation, and vascular issues and affects multiple organs [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. BD is characterized by persistent mucosal ulceration and systemic vasculitis and has demographic and clinical heterogeneity. Mortality is elevated due to complications in the pulmonary artery, large vessels, nerves, and gastrointestinal tract [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. KD manifests as an acute febrile illness and systemic vasculitis that primarily affects young children typically ranging from 6 months to 4 years of age [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Initially perceived as self-limiting [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], KD is now acknowledged as a systemic vasculitis with a propensity to cause the development of coronary artery lesions, affecting up to 25% of untreated patients [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. The emergence of coronary artery lesions associated with KD is a major contributor to pediatric heart disease in developed nations. GPA, formerly known as wegner's granulomatosis, represents a rare form of necrotizing vasculitis affecting small- to medium-sized vessels. This condition predominantly affects the upper and lower respiratory tracts, as well as the renal system [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. The formation of necrotizing granulomas in regions around the head, neck, and kidneys can result in diverse and numerous clinical manifestations.\u003c/p\u003e \u003cp\u003eGut dysbiosis, which influences mucosal immune homeostasis and gut barrier integrity, may contribute to autoimmune disorders. The established link between the microbiome and autoimmune diseases, such as systemic lupus erythematosus and rheumatoid arthritis, offers a promising therapeutic target [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Vasculitis patients exhibit dysbiosis compared to those of individuals with healthy controls. Crucially, environmental triggers, including changes in the gut microbiota, play a significant role in vasculitis onset, with an imbalance in the intestinal microbiome occurring with an increased abundance of pathogenic bacteria and a decreased abundance of beneficial bacteria [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Nevertheless, investigations of the microbiome in blood and the aorta have revealed varying abundances of \u003cem\u003eActinobacteria\u003c/em\u003e, \u003cem\u003eProteobacteria\u003c/em\u003e, \u003cem\u003eBifidobacterium\u003c/em\u003e, and \u003cem\u003eParasutterella\u003c/em\u003e and minimal \u003cem\u003eBacteroidetes\u003c/em\u003e, \u003cem\u003eRhodococcus\u003c/em\u003e, \u003cem\u003eCytophagaceae\u003c/em\u003e, and \u003cem\u003eGranulicatella\u003c/em\u003e among GCA patients [\u003cspan additionalcitationids=\"CR13 CR14\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Several cross-sectional studies have indicated alterations in gut bacterial abundance in patients with BD compared to healthy controls. These findings revealed an enrichment of lactic acid-producing bacteria, sulfate-reducing bacteria, and certain opportunistic pathogens in the gut microbiota of BD patients; conversely, there was a deficiency in butyric acid-producing bacteria and methanogenic bacteria [\u003cspan additionalcitationids=\"CR17 CR18 CR19 CR20 CR21 CR22 CR23\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. KD arises from the interplay of genetic and environmental susceptibility factors alongside infectious triggers. Several researchers have proposed that KD induces microbial dysbiosis, diminishing the production of short-chain fatty acids (SCFAs) by the intestinal microbiota. This alteration potentially leads to an imbalance between helper T cells 17 and regulatory T cells, contributing to the pathogenesis of KD [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Dekkema et al. conducted a comprehensive review of nasal microbiome studies in patients with ANCA-associated vasculitis (AAV), encompassing those with GPA. They found that nasal microbial dysbiosis is prevalent in active AAV patients, and immunosuppressive treatment thus has the potential to ameliorate this disturbance [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Notably, investigations on GPA and the gut microbiota are lacking.\u003c/p\u003e \u003cp\u003eThe inherent limitations within the designs of observational studies pose challenges in establishing definitive causal relationships. Constraints such as potential influence from confounding variables and biases, limited sample sizes, and variations in ethnic demographics hinder the ability to conclusively establish causality. Consequently, the causative nature of the relationship between the gut microbiota and immune-related vasculitis remains unclear. Furthermore, the directionality of this relationship\u0026mdash;whether accidental, bidirectional, or unidirectional\u0026mdash;remains ambiguous. Mendelian randomization (MR) is a contemporary epidemiological approach employed when the execution of randomized controlled trials is impractical. The primary data source for MR is derived from the Human Genome Project, and genetic variants are utilized as instrumental variables (IVs) to mitigate the limitations inherent in observational studies. By extracting single-nucleotide polymorphisms (SNPs) from GWASs, MR establishes a link between exposure and outcome while employing analytical techniques to mitigate confounding factors [\u003cspan additionalcitationids=\"CR29\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. MR investigations, akin to randomized controlled trials, yield results less susceptible to reverse causation and residual bias [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. An increase in the number of GWASs related to the gut microbiota and disease [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] has led to the widespread availability of large-scale summary statistics, facilitating two-sample MR analysis with significantly enhanced statistical power.\u003c/p\u003e \u003cp\u003eIn this study, we explored the causal connection between the gut microbiota and various immune-related vasculitis conditions through an extensive two-sample MR analysis involving four distinct vasculitis types (GCA, BD, KD, and GPA). Utilizing a bidirectional MR approach, we sought to investigate the potential causal impact of the gut microbiota on the risk of immune-related vasculitis and simultaneously assess whether genetic predisposition to immune-related vasculitis risk causally influences the composition of the gut microbiota. These analyses aimed to elucidate the role of the gut microbiota in the development of immune-related vasculitis, contributing to the eventual formulation of novel treatment strategies.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Study design\u003c/h2\u003e \u003cp\u003eWe conducted a two-sample MR analysis to explore the potential causal link between immune-related vasculitis and the gut microbiome. The study's workflow is depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. To ensure the validity of the IVs, we adhered to the three foundational assumptions of the MR design: (I) the genetic variation used as an IV must exhibit a significant association with the exposure(s); (ii) the genetic variation must be independent of confounding factors; and (iii) the variation must solely relate to the outcome(s) through the exposure(s) [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Initial emphasis was placed on establishing causation, treating the gut microbiome as the exposure and immune-related vasculitis as the outcome. Subsequently, we analyzed the reverse causal direction, considering immune-related vasculitis as the exposure and the gut microbiome as the outcome (refer to Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e for detailed insights).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. GWAS data sources\u003c/h2\u003e \u003cp\u003eThe international consortium MiBioGen conducted a comprehensive genome-wide meta-analysis of gut microbiota-related GWAS data involving 18,340 participants from 24 cohorts representing diverse ethnicities [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. The resulting dataset includes 211 GWAS summary statistics for bacterial taxa, covering 9 phyla, 16 classes, 20 orders, 35 families (including 3 with unknown classifications), and 131 genera (with 12 having unknown taxonomies). The detailed taxonomic categorizations are presented in Supplementary Table\u0026nbsp;1. In addition, MR analyses incorporated the remaining bacterial taxa from five alternative phyla and their subcategories, enhancing potential evidence for causality. After excluding 15 families and genera with unknown taxonomic classifications, a total of 196 taxa across diverse hierarchical levels were selected as the focal exposure of interest in our investigation. Additionally, GCA and BD summary statistics were derived from FinnGen Release 9 [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] using the phenocodes \"M13_GIANTCELL\" and \"M13_BEHCET\". The GCA dataset included 366,529 samples, comprising 996 cases and 365,533 controls, while the BD dataset included 365,618 samples, comprising 85 cases and 365,533 controls. Summary data for KD were acquired from the IEU OpenGWAS database available at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://gwas.mrcieu.ac.uk/\u003c/span\u003e\u003cspan address=\"https://gwas.mrcieu.ac.uk/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. The KD dataset included 6,190 samples, comprising 119 cases and 6,071 controls and encompassing a total of 152,542 genotyped SNPs [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Summary data for GPA were acquired from the publicly available GWAS catalog (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ebi.ac.uk/gwas/\u003c/span\u003e\u003cspan address=\"https://www.ebi.ac.uk/gwas/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The GPA dataset included 456,348 samples, comprising 135 cases and 456,213 controls [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. IV selection\u003c/h2\u003e \u003cp\u003eTo identify potential associations between exposure and outcome, we employed stringent criteria for selecting SNPs as IVs. When considering the gut microbiome as the exposure factor, a p value\u0026thinsp;\u0026lt;\u0026thinsp;1 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e was considered to indicate statistical significance. Additionally, the linkage disequilibrium threshold was established at r\u0026sup2; \u0026lt; 0.01, and the search distance for linkage disequilibrium r\u0026sup2; values was limited to 500 kb. To assess the potential causal impact of immune-related vasculitis on the bacterial genera, we conducted a reverse MR analysis. In the case of immune-related vasculitis being the exposure, the significance level for IVs was set at a p value\u0026thinsp;\u0026lt;\u0026thinsp;1 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e. The linkage disequilibrium threshold and clumping window were defined as r\u0026sup2; \u0026lt; 0.01 and 250 kb, respectively. To assess potential weak IV bias, the F-statistic of the IVs was computed, with a threshold F-statistic\u0026thinsp;\u0026lt;\u0026thinsp;10 indicating weak IV bias [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. IVs failing to meet this criterion (F-statistic\u0026thinsp;\u0026lt;\u0026thinsp;10) were excluded to ensure robustness. The dataset was further refined by removing ambiguous and palindromic SNPs.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. MR analyses\u003c/h2\u003e \u003cp\u003eMR analyses were performed employing various methods, including inverse variance-weighted (IVW), weighted median, MR\u0026ndash;Egger, and maximum likelihood approaches, to discern associations between the gut microbiome and three subtypes of immune-related vasculitis. The IVW approach, with an assumption of the validity of all SNPs as variables, served as the primary method. The weighted median approach provides consistent estimates under the assumption that more than half of the weights originate from valid SNPs [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. MR\u0026ndash;Egger analysis, which is capable of calibrating for pleiotropy, facilitates causal inference even in the presence of pleiotropic genetic variants [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. The maximum likelihood-based approach was employed to ensure appropriate confidence interval (CI) estimation in the presence of weak IVs. Interpretative guidelines for these methods can be found elsewhere [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. In sensitivity analyses, heterogeneity was assessed to gauge the compatibility of instrumental variables. Cochran\u0026rsquo;s Q statistics, implemented through the IVW and MR\u0026ndash;Egger methods, were used to test for heterogeneity, and consideration of its effect was warranted if it was present among IVs (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. The identification of horizontal pleiotropy, signaling that IVs are associated with outcomes through mechanisms other than causal effects and potentially leading to false-positive results (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e], was considered crucial. Direct association testing between selected IVs and outcomes involved horizontal pleiotropy assessment using MR pleiotropy residual sum and outlier (MR-PRESSO). Leave-one-out analysis was also conducted to determine whether a single SNP disproportionately influenced the causal effect of exposure on outcomes. This approach involved iteratively omitting each SNP from IVs during IVW testing and assessing potential outliers using the TwoSampleMR package (version 0.5.7) [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. False discovery rate (FDR) correction was applied via the q value procedure, with the threshold set as a q value\u0026thinsp;\u0026lt;\u0026thinsp;0.1 [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Taxa associations between the gut microbiota and immune-related vasculitis were considered suggestive if p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 but q\u0026thinsp;\u0026ge;\u0026thinsp;0.1. Specific FDR thresholds were established for various taxonomic levels: phyla (9), classes (16), orders (20), families (32), and genera (119). All analyses were conducted using R software (version 4.3.1). Venn diagram analysis was carried out using SUMO online software (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://angiogenesis.dkfz.de/oncoexpress/software/sumo/\u003c/span\u003e\u003cspan address=\"https://angiogenesis.dkfz.de/oncoexpress/software/sumo/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n\u003ch2\u003e3.1. SNP selection\u003c/h2\u003e\n\u003cp\u003eFollowing our screening criteria, 27,548 SNPs were identified as IVs through a large-scale GWAS. A comprehensive set of 196 taxa spanning five biological classifications (phylum, class, order, family, and genus) was chosen as the exposure conditions. In the context of immune-related vasculitis as the exposure, 10 SNPs for BD, 89 SNPs for GCA, 35 SNPs for KD, and 77 SNPs for GPA were selected as IVs. All IVs exhibited F statistics well above 10 (refer to Supplementary Table\u0026nbsp;2), indicating an absence of weak instrument bias. In investigating the causal relationship between the gut microbiota and immune-related vasculitis, the primary interpretation relied on the IVW results, complemented by findings from four additional tests: detailed results from the IVW, weighted median, MR\u0026ndash;Egger, and maximum likelihood approaches, along with outcomes regarding heterogeneity and pleiotropy, are available in Supplementary Tables\u0026nbsp;3\u0026ndash;10. In the case of heterogeneity or pleiotropy (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), any IV displaying such characteristics was excluded (see Supplementary Tables\u0026nbsp;11 and 12).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n\u003ch2\u003e3.2. Positive causal effects of the gut microbiota on immune-related vasculitis, determined after FDR correction\u003c/h2\u003e\n\u003cp\u003eAfter FDR correction, the results of IVW analyses demonstrated that the abundance of the genus \u003cem\u003eRuminococcaceae NK4A214 group\u003c/em\u003e (OR\u0026thinsp;=\u0026thinsp;0.734, 95% CI\u0026thinsp;=\u0026thinsp;0.614\u0026ndash;0.877, p\u0026thinsp;=\u0026thinsp;0.001, q\u0026thinsp;=\u0026thinsp;0.081) was negatively associated with the risk of GCA. Additionally, the abundances of the families \u003cem\u003eClostridiaceae 1\u003c/em\u003e (OR\u0026thinsp;=\u0026thinsp;1.699, 95% CI\u0026thinsp;=\u0026thinsp;1.314\u0026ndash;2.196, p\u0026thinsp;=\u0026thinsp;5.33E-05, q\u0026thinsp;=\u0026thinsp;0.001) and \u003cem\u003eActinomycetaceae\u003c/em\u003e (OR\u0026thinsp;=\u0026thinsp;2.333, 95% CI\u0026thinsp;=\u0026thinsp;1.899\u0026ndash;2.867, p\u0026thinsp;=\u0026thinsp;7.68E-16, q\u0026thinsp;=\u0026thinsp;1.23E-14) were positively associated with the risk of GPA. Moreover, the abundances of the class \u003cem\u003eLentisphaeria\u003c/em\u003e (OR\u0026thinsp;=\u0026thinsp;0.649, 95% CI\u0026thinsp;=\u0026thinsp;0.545\u0026ndash;0.773, p\u0026thinsp;=\u0026thinsp;1.28E-06, q\u0026thinsp;=\u0026thinsp;4.08E-06), class \u003cem\u003eMelainabacteria\u003c/em\u003e (OR\u0026thinsp;=\u0026thinsp;0.675, 95% CI\u0026thinsp;=\u0026thinsp;0.567\u0026ndash;0.804, p\u0026thinsp;=\u0026thinsp;1.00E-05, q\u0026thinsp;=\u0026thinsp;2.67E-05), class \u003cem\u003eNegativicutes\u003c/em\u003e (OR\u0026thinsp;=\u0026thinsp;0.696, 95% CI\u0026thinsp;=\u0026thinsp;0.51\u0026ndash;0.951, p\u0026thinsp;=\u0026thinsp;0.023, q\u0026thinsp;=\u0026thinsp;0.04), family \u003cem\u003eLachnospiraceae\u003c/em\u003e (OR\u0026thinsp;=\u0026thinsp;0.653, 95% CI\u0026thinsp;=\u0026thinsp;0.515\u0026ndash;0.828, p\u0026thinsp;=\u0026thinsp;4.30E-04, q\u0026thinsp;=\u0026thinsp;0.003), family \u003cem\u003ePorphyromonadaceae\u003c/em\u003e (OR\u0026thinsp;=\u0026thinsp;0.503, 95% CI\u0026thinsp;=\u0026thinsp;0.309\u0026ndash;0.818, p\u0026thinsp;=\u0026thinsp;5.650E-03, q\u0026thinsp;=\u0026thinsp;0.03), family \u003cem\u003eRuminococcaceae\u003c/em\u003e (OR\u0026thinsp;=\u0026thinsp;0.442, 95% CI\u0026thinsp;=\u0026thinsp;0.233\u0026ndash;0.838, p\u0026thinsp;=\u0026thinsp;0.012, q\u0026thinsp;=\u0026thinsp;0.06), and family \u003cem\u003eStreptococcaceae\u003c/em\u003e (OR\u0026thinsp;=\u0026thinsp;0.4, 95% CI\u0026thinsp;=\u0026thinsp;0.23\u0026ndash;0.695, p\u0026thinsp;=\u0026thinsp;0.001, q\u0026thinsp;=\u0026thinsp;0.007) were negatively associated with the risk of GPA (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). In reverse MR analysis, BD was found to be positively associated with the risk of the family \u003cem\u003eStreptococcaceae\u003c/em\u003e (OR\u0026thinsp;=\u0026thinsp;1.021, 95% CI\u0026thinsp;=\u0026thinsp;0.996\u0026ndash;1.022, p\u0026thinsp;=\u0026thinsp;0.002, q\u0026thinsp;=\u0026thinsp;0.065) (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003ePositive MR results of the causal relationship between gut microbiota and GPA and GCA risk after FDR correction.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eTaxa\u003c/p\u003e\n\u003c/th\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eGut microbiota (exposure)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eOutcome\u003c/p\u003e\n\u003c/th\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eMethods\u003c/p\u003e\n\u003c/th\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eSNPs (n)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eOR (95% CI)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\n\u003c/th\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eq-value\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eTest of heterogeneity\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eTest of pleiotropy\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eCochran\u0026rsquo;s Q\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eEgger intercept\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eSE\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGenus\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eRuminococcaceae NK4A214 group\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGCA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMR Egger\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e105\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.648(0.363\u0026ndash;1.155)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.144\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e96.834\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.652\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.008\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.018\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.659\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWeighted median\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e105\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.726(0.56\u0026ndash;0.941)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.016\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.927\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIVW\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e105\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.734(0.614\u0026ndash;0.877)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.081\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e97.031\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.673\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSimple mode\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e105\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.784(0.39\u0026ndash;1.579)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.497\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWeighted mode\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e105\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.537(0.286\u0026ndash;1.01)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.057\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eClass\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eNegativicutes\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGPA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMR Egger\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e310\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.439(0.16\u0026ndash;1.205)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.111\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.296\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e288.058\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.787\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.028\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.03\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.348\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWeighted median\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e310\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.13(0.717\u0026ndash;1.781)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.599\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.599\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIVW\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e310\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.696(0.51\u0026ndash;0.951)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.023\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.040\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e288.942\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.788\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSimple mode\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e310\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3.006(0.602\u0026ndash;15.005)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.181\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.321\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWeighted mode\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e310\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.904(0.603\u0026ndash;13.98)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.185\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.657\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eClass\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eMelainabacteria\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGPA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMR Egger\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e327\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.854(0.423\u0026ndash;1.725)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.66\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.094\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e243.851\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-0.022\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.03\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.499\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWeighted median\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e327\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.644(0.506\u0026ndash;0.819)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.35E-04\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8.94E-04\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIVW\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e327\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.675(0.567\u0026ndash;0.804)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e1.00E-05\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e2.67E-05\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e244.31\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSimple mode\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e327\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.467(0.599\u0026ndash;3.594)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.403\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.500\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWeighted mode\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e327\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.435(0.608\u0026ndash;3.389)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.411\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eClass\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eLentisphaeria\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGPA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMR Egger\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e300\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.465(0.625\u0026ndash;3.436)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.38\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.608\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e331.138\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.091\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-0.086\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.05\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.056\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWeighted median\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e300\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.586(0.459\u0026ndash;0.747)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.68E-05\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.38E-05\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIVW\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e300\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.649(0.545\u0026ndash;0.773)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e1.28E-06\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e4.08E-06\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e335.213\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.073\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSimple mode\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e300\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.164(0.053\u0026ndash;0.512)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.03E-03\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.008\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWeighted mode\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e300\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.172(0.065\u0026ndash;0.451)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.06E-04\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.002\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFamily\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eStreptococcaceae\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGPA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMR Egger\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e89\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.139(0.036\u0026ndash;0.542)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.53E-03\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.059\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e101.063\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.144\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.081\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.05\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.1\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWeighted median\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e89\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.428(0.197\u0026ndash;0.926)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.031\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.125\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIVW\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e89\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.4(0.23\u0026ndash;0.695)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.007\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e104.275\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.114\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSimple mode\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e89\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.377(0.05\u0026ndash;2.845)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.347\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.925\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWeighted mode\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e89\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.336(0.063\u0026ndash;1.799)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.206\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.732\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFamily\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003ePorphyromonadaceae\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGPA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMR Egger\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e112\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.56(0.146\u0026ndash;2.151)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.854\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e107.26\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.556\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-0.007\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.04\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.867\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWeighted median\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e112\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.613(0.291\u0026ndash;1.293)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.199\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.454\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIVW\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e112\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.503(0.309\u0026ndash;0.818)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e5.65E-03\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.030\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e107.288\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.582\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSimple mode\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e112\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.579(0.089\u0026ndash;3.763)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.568\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWeighted mode\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e112\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.695(0.099\u0026ndash;4.902)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.716\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFamily\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eLachnospiraceae\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGPA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMR Egger\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e533\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.076(0.474\u0026ndash;2.446)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.861\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.888\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e287.359\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-0.029\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.02\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.213\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWeighted median\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e533\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.515(0.372\u0026ndash;0.715)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6.98E-05\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.46E-04\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIVW\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e533\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.653(0.515\u0026ndash;0.828)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e4.30E-04\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.003\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e288.917\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSimple mode\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e533\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.427(0.117\u0026ndash;1.554)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.197\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.631\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWeighted mode\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e533\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.441(0.123\u0026ndash;1.59)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.212\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.677\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFamily\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eClostridiaceae 1\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGPA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMR Egger\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e344\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.869(0.373\u0026ndash;2.029)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.746\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.880\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e320.495\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.792\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.045\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.03\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.105\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWeighted median\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e344\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.807(1.243\u0026ndash;2.626)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.002\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.010\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIVW\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e344\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.699(1.314\u0026ndash;2.196)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e5.33E-05\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e323.139\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.773\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSimple mode\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e344\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.472(0.399\u0026ndash;5.44)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.562\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWeighted mode\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e344\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.472(0.358\u0026ndash;6.053)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.592\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.997\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFamily\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eActinomycetaceae\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGPA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMR Egger\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e332\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.262(0.992\u0026ndash;5.157)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.053\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.243\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e369.839\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.064\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.003\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.03\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.939\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWeighted median\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e332\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.867(2.125\u0026ndash;3.87)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.71E-12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.83E-10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIVW\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e332\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.333(1.899\u0026ndash;2.867)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e7.68E-16\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e1.23E-14\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e369.846\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.069\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSimple mode\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e332\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e11.45(3.591\u0026ndash;36.505)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.77E-05\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.002\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWeighted mode\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e332\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e10.909(3.77-31.567)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.41E-05\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.52E-04\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFamily\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eRuminococcaceae\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGPA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMR Egger\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e75\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.645(0.105\u0026ndash;3.949)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.637\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.927\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e84.008\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.178\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-0.025\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.057\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.663\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWeighted median\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e75\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.599(0.232\u0026ndash;1.547)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.29\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.580\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIVW\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e75\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.442(0.233\u0026ndash;0.838)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.012\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.060\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e84.229\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.195\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSimple mode\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e75\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.043(0.101\u0026ndash;10.747)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.972\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.972\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWeighted mode\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e75\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.969(0.149\u0026ndash;6.301)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.974\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"13\"\u003eGCA: giant cell arteritis; GPA: granulomatosis with polyangiitis; FDR: False discovery rate; MR: mendelian randomization; IVW: inverse variance weighted; SNPs: single nucleotide polymorphisms; OR: odds ratio; CI: confidence intervals; SE: standard error.\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003ePositive MR results of the causal relationship between gut microbiota and BD risk after FDR correction.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eExposure\u003c/p\u003e\n\u003c/th\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eTaxa\u003c/p\u003e\n\u003c/th\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eGut microbiota (outcome)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eMethods\u003c/p\u003e\n\u003c/th\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eSNPs (n)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eOR (95% CI)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eTest of heterogeneity\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eTest of pleiotropy\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eCochran\u0026rsquo;s Q\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eEgger intercept\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eSE\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBD\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFamily\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eStreptococcaceae\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMR Egger\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.024(0.933\u0026ndash;1.094)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.569\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e5.046\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.753\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-0.002\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.026\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.935\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWeighted median\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.022(0.992\u0026ndash;1.028)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.015\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIVW\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.021(0.996\u0026ndash;1.022)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.002\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e5.053\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.830\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSimple mode\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.029(0.984\u0026ndash;1.042)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.085\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWeighted mode\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.025(0.983\u0026ndash;1.04)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.112\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"12\"\u003eBD: Behcet's disease; FDR: False discovery rate; MR: mendelian randomization; IVW: inverse variance weighted; SNPs: single nucleotide polymorphisms; OR: odds ratio; CI: confidence intervals; SE: standard error.\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n\u003ch2\u003e3.3. Potential causal effects of the gut microbiota on immune-related vasculitis\u003c/h2\u003e\n\u003cdiv id=\"Sec11\" class=\"Section3\"\u003e\n\u003ch2\u003e3.3.1. BD\u003c/h2\u003e\n\u003cp\u003e\u003cstrong\u003eForward MR analysis.\u003c/strong\u003e The results of IVW analyses demonstrated that the abundances of the class \u003cem\u003eMelainabacteria\u003c/em\u003e (odds ratio (OR)\u0026thinsp;=\u0026thinsp;1.851, 95% CI\u0026thinsp;=\u0026thinsp;0.838\u0026ndash;2.037, p\u0026thinsp;=\u0026thinsp;0.007), class \u003cem\u003eGammaproteobacteria\u003c/em\u003e (OR\u0026thinsp;=\u0026thinsp;1.949, 95% CI\u0026thinsp;=\u0026thinsp;0.695\u0026ndash;2.571, p\u0026thinsp;=\u0026thinsp;0.046), family \u003cem\u003eRhodospirillaceae\u003c/em\u003e (OR\u0026thinsp;=\u0026thinsp;1.744, 95% CI\u0026thinsp;=\u0026thinsp;0.764\u0026ndash;2.123, p\u0026thinsp;=\u0026thinsp;0.033), genus \u003cem\u003eRuminococcaceae UCG011\u003c/em\u003e (OR\u0026thinsp;=\u0026thinsp;1.533, 95% CI\u0026thinsp;=\u0026thinsp;0.837\u0026ndash;1.733, p\u0026thinsp;=\u0026thinsp;0.021), and genus \u003cem\u003eOdoribacter\u003c/em\u003e (OR\u0026thinsp;=\u0026thinsp;2.215, 95% CI\u0026thinsp;=\u0026thinsp;0.764\u0026ndash;2.613, p\u0026thinsp;=\u0026thinsp;0.011) were potentially positively associated with the risk of BD (Supplementary Table\u0026nbsp;3, Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eReverse MR analysis.\u003c/strong\u003e When considering BD as an exposure and the gut microbiota as an outcome, the results of IVW analyses demonstrated that BD was potentially positively associated with the risk of the class \u003cem\u003eBacilli\u003c/em\u003e (OR\u0026thinsp;=\u0026thinsp;1.018, 95% CI\u0026thinsp;=\u0026thinsp;0.995\u0026ndash;1.021, p\u0026thinsp;=\u0026thinsp;0.008), order \u003cem\u003eLactobacillales\u003c/em\u003e (OR\u0026thinsp;=\u0026thinsp;1.018, 95% CI\u0026thinsp;=\u0026thinsp;0.995\u0026ndash;1.021, p\u0026thinsp;=\u0026thinsp;0.007), genus \u003cem\u003eHoldemanella\u003c/em\u003e (OR\u0026thinsp;=\u0026thinsp;1.022, 95% CI\u0026thinsp;=\u0026thinsp;0.99\u0026ndash;1.029, p\u0026thinsp;=\u0026thinsp;0.028), and genus \u003cem\u003eStreptococcus\u003c/em\u003e (OR\u0026thinsp;=\u0026thinsp;1.018, 95% CI\u0026thinsp;=\u0026thinsp;0.995\u0026ndash;1.021, p\u0026thinsp;=\u0026thinsp;0.007) (Supplementary Table\u0026nbsp;4, Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). Moreover, BD was potentially negatively associated with the risk of the phylum \u003cem\u003eVerrucomicrobia\u003c/em\u003e (OR\u0026thinsp;=\u0026thinsp;0.984, 95% CI\u0026thinsp;=\u0026thinsp;0.978\u0026ndash;1.008, p\u0026thinsp;=\u0026thinsp;0.043), the class \u003cem\u003eVerrucomicrobiae\u003c/em\u003e (OR\u0026thinsp;=\u0026thinsp;0.984, 95% CI\u0026thinsp;=\u0026thinsp;0.978\u0026ndash;1.009, p\u0026thinsp;=\u0026thinsp;0.045), the order \u003cem\u003eVerrucomicrobiales\u003c/em\u003e (OR\u0026thinsp;=\u0026thinsp;0.984, 95% CI\u0026thinsp;=\u0026thinsp;0.978\u0026ndash;1.009, p\u0026thinsp;=\u0026thinsp;0.045), the family \u003cem\u003eVerrucomicrobiaceae\u003c/em\u003e (OR\u0026thinsp;=\u0026thinsp;0.984, 95% CI\u0026thinsp;=\u0026thinsp;0.978\u0026ndash;1.009, p\u0026thinsp;=\u0026thinsp;0.045), the genus \u003cem\u003eAkkermansia\u003c/em\u003e (OR\u0026thinsp;=\u0026thinsp;0.984, 95% CI\u0026thinsp;=\u0026thinsp;0.978\u0026ndash;1.009, p\u0026thinsp;=\u0026thinsp;0.042), the genus \u003cem\u003eRuminiclostridium 6\u003c/em\u003e (OR\u0026thinsp;=\u0026thinsp;0.984, 95% CI\u0026thinsp;=\u0026thinsp;0.979\u0026ndash;1.008, p\u0026thinsp;=\u0026thinsp;0.029), and the genus \u003cem\u003eCoprococcus 3\u003c/em\u003e (OR\u0026thinsp;=\u0026thinsp;0.985, 95% CI\u0026thinsp;=\u0026thinsp;0.98\u0026ndash;1.007, p\u0026thinsp;=\u0026thinsp;0.028) (Supplementary Table\u0026nbsp;4, Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section3\"\u003e\n\u003ch2\u003e3.3.2. GCA\u003c/h2\u003e\n\u003cp\u003e\u003cstrong\u003eForward MR analysis.\u003c/strong\u003e The results of IVW analyses demonstrated that the genera \u003cem\u003eLachnospiraceae UCG004\u003c/em\u003e (OR\u0026thinsp;=\u0026thinsp;1.214, 95% CI\u0026thinsp;=\u0026thinsp;1.015\u0026ndash;1.453, p\u0026thinsp;=\u0026thinsp;0.034), \u003cem\u003eRuminococcus 2\u003c/em\u003e (OR\u0026thinsp;=\u0026thinsp;1.199, 95% CI\u0026thinsp;=\u0026thinsp;1.003\u0026ndash;1.434, p\u0026thinsp;=\u0026thinsp;0.047), \u003cem\u003eRuminococcus gnavus group\u003c/em\u003e (OR\u0026thinsp;=\u0026thinsp;1.142, 95% CI\u0026thinsp;=\u0026thinsp;1.018\u0026ndash;1.28, p\u0026thinsp;=\u0026thinsp;0.023), \u003cem\u003eHoldemania\u003c/em\u003e (OR\u0026thinsp;=\u0026thinsp;1.225, 95% CI\u0026thinsp;=\u0026thinsp;1.044\u0026ndash;1.437, p\u0026thinsp;=\u0026thinsp;0.013), \u003cem\u003eFlavonifractor\u003c/em\u003e (OR\u0026thinsp;=\u0026thinsp;1.225, 95% CI\u0026thinsp;=\u0026thinsp;1.048\u0026ndash;1.432, p\u0026thinsp;=\u0026thinsp;0.011), \u003cem\u003eDialister\u003c/em\u003e (OR\u0026thinsp;=\u0026thinsp;1.2, 95% CI\u0026thinsp;=\u0026thinsp;1.029-1.4, p\u0026thinsp;=\u0026thinsp;0.02), \u003cem\u003eBilophila\u003c/em\u003e (OR\u0026thinsp;=\u0026thinsp;1.226, 95% CI\u0026thinsp;=\u0026thinsp;1.043\u0026ndash;1.44, p\u0026thinsp;=\u0026thinsp;0.013), and \u003cem\u003eEubacterium nodatum group\u003c/em\u003e (OR\u0026thinsp;=\u0026thinsp;1.148, 95% CI\u0026thinsp;=\u0026thinsp;1.048\u0026ndash;1.257, p\u0026thinsp;=\u0026thinsp;0.003) were potentially positively associated with the risk of GCA (Supplementary Table\u0026nbsp;5, Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). Moreover, the phylum \u003cem\u003eVerrucomicrobia\u003c/em\u003e (OR\u0026thinsp;=\u0026thinsp;0.796, 95% CI\u0026thinsp;=\u0026thinsp;0.673\u0026ndash;0.943, p\u0026thinsp;=\u0026thinsp;0.008), family \u003cem\u003ePorphyromonadaceae\u003c/em\u003e (OR\u0026thinsp;=\u0026thinsp;0.833, 95% CI\u0026thinsp;=\u0026thinsp;0.696\u0026ndash;0.998, p\u0026thinsp;=\u0026thinsp;0.048), genus \u003cem\u003eVeillonella\u003c/em\u003e (OR\u0026thinsp;=\u0026thinsp;0.872, 95% CI\u0026thinsp;=\u0026thinsp;0.768\u0026ndash;0.991, p\u0026thinsp;=\u0026thinsp;0.036), genus \u003cem\u003eErysipelatoclostridium\u003c/em\u003e (OR\u0026thinsp;=\u0026thinsp;0.835, 95% CI\u0026thinsp;=\u0026thinsp;0.72\u0026ndash;0.968, p\u0026thinsp;=\u0026thinsp;0.017), and genus \u003cem\u003eAdlercreutzia\u003c/em\u003e (OR\u0026thinsp;=\u0026thinsp;0.858, 95% CI\u0026thinsp;=\u0026thinsp;0.745\u0026ndash;0.988, p\u0026thinsp;=\u0026thinsp;0.034) were potentially negatively associated with the risk of GCA (Supplementary Table\u0026nbsp;5, Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eReverse MR analysis.\u003c/strong\u003e When GCA was considered an exposure and the gut microbiota was considered an outcome, the results of IVW analyses demonstrated that GCA was potentially positively associated with the risk of the genus \u003cem\u003eErysipelotrichaceae UCG003\u003c/em\u003e (OR\u0026thinsp;=\u0026thinsp;1.079, 95% CI\u0026thinsp;=\u0026thinsp;0.987\u0026ndash;1.083, p\u0026thinsp;=\u0026thinsp;0.001) (Supplementary Table\u0026nbsp;6, Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). Moreover, GCA was potentially negatively associated with the risk of the family \u003cem\u003eVictivallaceae\u003c/em\u003e (OR\u0026thinsp;=\u0026thinsp;0.974, 95% CI\u0026thinsp;=\u0026thinsp;0.964\u0026ndash;1.014, p\u0026thinsp;=\u0026thinsp;0.044), genus \u003cem\u003eAlistipes\u003c/em\u003e (OR\u0026thinsp;=\u0026thinsp;0.985, 95% CI\u0026thinsp;=\u0026thinsp;0.982\u0026ndash;1.005, p\u0026thinsp;=\u0026thinsp;0.011), and genus \u003cem\u003eRuminococcaceae UCG010\u003c/em\u003e (OR\u0026thinsp;=\u0026thinsp;0.986, 95% CI\u0026thinsp;=\u0026thinsp;0.98\u0026ndash;1.008, p\u0026thinsp;=\u0026thinsp;0.046) (Supplementary Table\u0026nbsp;6, Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section3\"\u003e\n\u003ch2\u003e3.3.3. KD\u003c/h2\u003e\n\u003cp\u003e\u003cstrong\u003eForward MR analysis.\u003c/strong\u003e The results of IVW analyses demonstrated that the phylum \u003cem\u003eLentisphaerae\u003c/em\u003e (OR\u0026thinsp;=\u0026thinsp;2.8, 95% CI\u0026thinsp;=\u0026thinsp;0.675\u0026ndash;3.623, p\u0026thinsp;=\u0026thinsp;0.016), genus \u003cem\u003eLachnospira\u003c/em\u003e (OR\u0026thinsp;=\u0026thinsp;5.506, 95% CI\u0026thinsp;=\u0026thinsp;0.522\u0026ndash;8.428, p\u0026thinsp;=\u0026thinsp;0.016), and genus \u003cem\u003eVictivallis\u003c/em\u003e (OR\u0026thinsp;=\u0026thinsp;2.368, 95% CI\u0026thinsp;=\u0026thinsp;0.785\u0026ndash;2.692, p\u0026thinsp;=\u0026thinsp;0.006) were potentially positively associated with the risk of KD (Supplementary Table\u0026nbsp;7, Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). Moreover, the family \u003cem\u003ePrevotellaceae\u003c/em\u003e (OR\u0026thinsp;=\u0026thinsp;0.094, 95% CI\u0026thinsp;=\u0026thinsp;0.053\u0026ndash;2.416, p\u0026thinsp;=\u0026thinsp;0.015), genus \u003cem\u003eRuminiclostridium 9\u003c/em\u003e (OR\u0026thinsp;=\u0026thinsp;0.217, 95% CI\u0026thinsp;=\u0026thinsp;0.131\u0026ndash;2.023, p\u0026thinsp;=\u0026thinsp;0.029), genus \u003cem\u003eLactobacillus\u003c/em\u003e (OR\u0026thinsp;=\u0026thinsp;0.317, 95% CI\u0026thinsp;=\u0026thinsp;0.205\u0026ndash;1.798, p\u0026thinsp;=\u0026thinsp;0.038), and genus \u003cem\u003eBifidobacterium\u003c/em\u003e (OR\u0026thinsp;=\u0026thinsp;0.196, 95% CI\u0026thinsp;=\u0026thinsp;0.098\u0026ndash;2.473, p\u0026thinsp;=\u0026thinsp;0.048) were potentially negatively associated with the risk of KD (Supplementary Table\u0026nbsp;7, Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eReverse MR analysis.\u003c/strong\u003e When KD was considered an exposure and the gut microbiota was considered an outcome, the results of IVW analyses demonstrated that KD was potentially positively associated with the risk of the genus \u003cem\u003eEubacterium oxidoreducens group\u003c/em\u003e (OR\u0026thinsp;=\u0026thinsp;1.013, 95% CI\u0026thinsp;=\u0026thinsp;0.993\u0026ndash;1.018, p\u0026thinsp;=\u0026thinsp;0.041) (Supplementary Table\u0026nbsp;8, Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). Moreover, KD was potential negatively associated with the risk of the order \u003cem\u003eBacillales\u003c/em\u003e (OR\u0026thinsp;=\u0026thinsp;0.979, 95% CI\u0026thinsp;=\u0026thinsp;0.976\u0026ndash;1.005, p\u0026thinsp;=\u0026thinsp;0.005), order \u003cem\u003eActinomycetales\u003c/em\u003e (OR\u0026thinsp;=\u0026thinsp;0.988, 95% CI\u0026thinsp;=\u0026thinsp;0.985\u0026ndash;1.004, p\u0026thinsp;=\u0026thinsp;0.01), family \u003cem\u003eActinomycetaceae\u003c/em\u003e (OR\u0026thinsp;=\u0026thinsp;0.988, 95% CI\u0026thinsp;=\u0026thinsp;0.985\u0026ndash;1.004, p\u0026thinsp;=\u0026thinsp;0.01), family \u003cem\u003eRuminococcaceae\u003c/em\u003e (OR\u0026thinsp;=\u0026thinsp;0.994, 95% CI\u0026thinsp;=\u0026thinsp;0.991\u0026ndash;1.003, p\u0026thinsp;=\u0026thinsp;0.044), family \u003cem\u003eDefluviitaleaceae\u003c/em\u003e (OR\u0026thinsp;=\u0026thinsp;0.987, 95% CI\u0026thinsp;=\u0026thinsp;0.985\u0026ndash;1.004, p\u0026thinsp;=\u0026thinsp;0.006), family \u003cem\u003eRikenellaceae\u003c/em\u003e (OR\u0026thinsp;=\u0026thinsp;0.994, 95% CI\u0026thinsp;=\u0026thinsp;0.991\u0026ndash;1.004, p\u0026thinsp;=\u0026thinsp;0.048), genus \u003cem\u003eAlistipes\u003c/em\u003e (OR\u0026thinsp;=\u0026thinsp;0.993, 95% CI\u0026thinsp;=\u0026thinsp;0.99\u0026ndash;1.003, p\u0026thinsp;=\u0026thinsp;0.02), genus \u003cem\u003eEubacterium eligens group\u003c/em\u003e (OR\u0026thinsp;=\u0026thinsp;0.991, 95% CI\u0026thinsp;=\u0026thinsp;0.989\u0026ndash;1.003, p\u0026thinsp;=\u0026thinsp;0.017), genus \u003cem\u003eRuminococcus torques group\u003c/em\u003e (OR\u0026thinsp;=\u0026thinsp;0.993, 95% CI\u0026thinsp;=\u0026thinsp;0.991\u0026ndash;1.003, p\u0026thinsp;=\u0026thinsp;0.018), genus \u003cem\u003eRuminococcus 2\u003c/em\u003e (OR\u0026thinsp;=\u0026thinsp;0.993, 95% CI\u0026thinsp;=\u0026thinsp;0.991\u0026ndash;1.004, p\u0026thinsp;=\u0026thinsp;0.048), genus \u003cem\u003eStreptococcus\u003c/em\u003e (OR\u0026thinsp;=\u0026thinsp;0.993, 95% CI\u0026thinsp;=\u0026thinsp;0.991\u0026ndash;1.004, p\u0026thinsp;=\u0026thinsp;0.044), genus \u003cem\u003eActinomyces\u003c/em\u003e (OR\u0026thinsp;=\u0026thinsp;0.988, 95% CI\u0026thinsp;=\u0026thinsp;0.985\u0026ndash;1.004, p\u0026thinsp;=\u0026thinsp;0.014), genus \u003cem\u003eDefluviitaleaceae UCG011\u003c/em\u003e (OR\u0026thinsp;=\u0026thinsp;0.987, 95% CI\u0026thinsp;=\u0026thinsp;0.985\u0026ndash;1.003, p\u0026thinsp;=\u0026thinsp;0.005), genus \u003cem\u003eButyricimonas\u003c/em\u003e (OR\u0026thinsp;=\u0026thinsp;0.991, 95% CI\u0026thinsp;=\u0026thinsp;0.988\u0026ndash;1.005, p\u0026thinsp;=\u0026thinsp;0.037), and genus \u003cem\u003eRomboutsia\u003c/em\u003e (OR\u0026thinsp;=\u0026thinsp;0.992, 95% CI\u0026thinsp;=\u0026thinsp;0.99\u0026ndash;1.004, p\u0026thinsp;=\u0026thinsp;0.03) (Supplementary Table\u0026nbsp;8, Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section3\"\u003e\n\u003ch2\u003e3.3.4. GPA\u003c/h2\u003e\n\u003cp\u003e\u003cstrong\u003eForward MR analysis.\u003c/strong\u003e The results of IVW analyses demonstrated that the genera \u003cem\u003eTuricibacter\u003c/em\u003e (OR\u0026thinsp;=\u0026thinsp;1.465, 95% CI\u0026thinsp;=\u0026thinsp;1.001\u0026ndash;2.143, p\u0026thinsp;=\u0026thinsp;0.049), \u003cem\u003eParaprevotella\u003c/em\u003e (OR\u0026thinsp;=\u0026thinsp;1.513, 95% CI\u0026thinsp;=\u0026thinsp;1.049\u0026ndash;2.182, p\u0026thinsp;=\u0026thinsp;0.027), \u003cem\u003eParabacteroides\u003c/em\u003e (OR\u0026thinsp;=\u0026thinsp;1.899, 95% CI\u0026thinsp;=\u0026thinsp;1.17\u0026ndash;3.082, p\u0026thinsp;=\u0026thinsp;0.009), \u003cem\u003eChristensenellaceae R.7 group\u003c/em\u003e (OR\u0026thinsp;=\u0026thinsp;1.688, 95% CI\u0026thinsp;=\u0026thinsp;1.058\u0026ndash;2.693, p\u0026thinsp;=\u0026thinsp;0.029), \u003cem\u003eButyricimonas\u003c/em\u003e (OR\u0026thinsp;=\u0026thinsp;1.58, 95% CI\u0026thinsp;=\u0026thinsp;1.077\u0026ndash;2.316, p\u0026thinsp;=\u0026thinsp;0.019), and \u003cem\u003eAnaerotruncus\u003c/em\u003e (OR\u0026thinsp;=\u0026thinsp;1.684, 95% CI\u0026thinsp;=\u0026thinsp;1.019\u0026ndash;2.784, p\u0026thinsp;=\u0026thinsp;0.042) were potentially positively associated with the risk of GPA (Supplementary Table\u0026nbsp;9, Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). Moreover, the order \u003cem\u003eGastranaerophilales\u003c/em\u003e (OR\u0026thinsp;=\u0026thinsp;0.679, 95% CI\u0026thinsp;=\u0026thinsp;0.477\u0026ndash;0.966, p\u0026thinsp;=\u0026thinsp;0.031), family \u003cem\u003eFamily XIII\u003c/em\u003e (OR\u0026thinsp;=\u0026thinsp;0.602, 95% CI\u0026thinsp;=\u0026thinsp;0.369\u0026ndash;0.982, p\u0026thinsp;=\u0026thinsp;0.042), genus \u003cem\u003eLachnospira\u003c/em\u003e (OR\u0026thinsp;=\u0026thinsp;0.544, 95% CI\u0026thinsp;=\u0026thinsp;0.342\u0026ndash;0.865, p\u0026thinsp;=\u0026thinsp;0.01), genus \u003cem\u003eBlautia\u003c/em\u003e (OR\u0026thinsp;=\u0026thinsp;0.616, 95% CI\u0026thinsp;=\u0026thinsp;0.381\u0026ndash;0.997, p\u0026thinsp;=\u0026thinsp;0.048), and genus \u003cem\u003eBacteroides\u003c/em\u003e (OR\u0026thinsp;=\u0026thinsp;0.583, 95% CI\u0026thinsp;=\u0026thinsp;0.349\u0026ndash;0.975, p\u0026thinsp;=\u0026thinsp;0.04) were potentially negatively associated with the risk of GPA (Supplementary Table\u0026nbsp;9, Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eReverse MR analysis.\u003c/strong\u003e When GPA was considered an exposure and the gut microbiota was considered an outcome, the results of IVW analyses demonstrated that GPA was potentially positively associated with the risk of the genera \u003cem\u003eHoldemanella\u003c/em\u003e (OR\u0026thinsp;=\u0026thinsp;1.01, 95% CI\u0026thinsp;=\u0026thinsp;0.996\u0026ndash;1.012, p\u0026thinsp;=\u0026thinsp;0.02) and \u003cem\u003eBarnesiella\u003c/em\u003e (OR\u0026thinsp;=\u0026thinsp;1.006, 95% CI\u0026thinsp;=\u0026thinsp;0.997\u0026ndash;1.008, p\u0026thinsp;=\u0026thinsp;0.022) (Supplementary Table\u0026nbsp;10, Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). Moreover, GPA was potentially negatively associated with the risk of the genera \u003cem\u003eEubacterium oxidoreducens group\u003c/em\u003e (OR\u0026thinsp;=\u0026thinsp;0.99, 95% CI\u0026thinsp;=\u0026thinsp;0.987\u0026ndash;1.004, p\u0026thinsp;=\u0026thinsp;0.023), \u003cem\u003eRuminococcaceae UCG009\u003c/em\u003e (OR\u0026thinsp;=\u0026thinsp;0.992, 95% CI\u0026thinsp;=\u0026thinsp;0.989\u0026ndash;1.004, p\u0026thinsp;=\u0026thinsp;0.037), \u003cem\u003eLachnospiraceae UCG004\u003c/em\u003e (OR\u0026thinsp;=\u0026thinsp;0.993, 95% CI\u0026thinsp;=\u0026thinsp;0.992\u0026ndash;1.002, p\u0026thinsp;=\u0026thinsp;0.008), \u003cem\u003eRoseburia\u003c/em\u003e (OR\u0026thinsp;=\u0026thinsp;0.995, 95% CI\u0026thinsp;=\u0026thinsp;0.993\u0026ndash;1.002, p\u0026thinsp;=\u0026thinsp;0.037), and \u003cem\u003eVeillonella\u003c/em\u003e (OR\u0026thinsp;=\u0026thinsp;0.991, 95% CI\u0026thinsp;=\u0026thinsp;0.99\u0026ndash;1.003, p\u0026thinsp;=\u0026thinsp;0.008) (Supplementary Table\u0026nbsp;10, Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n\u003ch2\u003e3.4. Potential causal interactions between the gut microbiota and immune-related vasculitis\u003c/h2\u003e\n\u003cp\u003eTaken together, the positive association results from IVW analysis (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) revealed numerous shared gut microbiota constituents associated with immune-related vasculitis. Regardless of the role of the gut microbiota as an exposure or outcome, Venn diagrams illustrated intricate intersections among immune-related vasculitis subtypes (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). Consequently, an interaction diagram depicting the interplay of the gut microbiota in immune-related vasculitis was constructed (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv id=\"Sec16\" class=\"Section3\"\u003e\n\u003ch2\u003e3.4.1. The gut microbiota positively interacts with at least three types of immune-related vasculitis\u003c/h2\u003e\n\u003cp\u003eThe gut microbiota, particularly \u003cem\u003eRuminococcaceae/Ruminococcus\u003c/em\u003e, exhibits prominent interactions with immune-related vasculitis. Notably, the abundance of the family \u003cem\u003eRuminococcaceae\u003c/em\u003e demonstrated a negative association with the risk of GPA, and KD exhibited a negative association with the risk of the family \u003cem\u003eRuminococcaceae\u003c/em\u003e. Additionally, the abundances of specific genera within \u003cem\u003eRuminococcaceae\u003c/em\u003e, such as \u003cem\u003eRuminococcaceae UCG011\u003c/em\u003e, \u003cem\u003eRuminococcus gnavus group\u003c/em\u003e and \u003cem\u003eRuminococcus 2\u003c/em\u003e, exhibited positive associations with the risk of BD and GCA. Conversely, the abundance of \u003cem\u003eRuminococcaceae NK4A214 group\u003c/em\u003e exhibited a negative association with the risk of GCA. Furthermore, KD, GPA and GCA were negatively associated with the risk of the genera \u003cem\u003eRuminococcus 2\u003c/em\u003e, \u003cem\u003eRuminococcus torques group\u003c/em\u003e, \u003cem\u003eRuminococcaceae UCG009\u003c/em\u003e, and \u003cem\u003eRuminococcaceae UCG010\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003eThe second set of gut microbiota constituents interacting with immune-related vasculitis included \u003cem\u003eEubacterium\u003c/em\u003e, \u003cem\u003eLachnospira/Lachnospiraceae\u003c/em\u003e, and \u003cem\u003eHoldemanella/Holdemania\u003c/em\u003e. Specifically, the abundance of the genus \u003cem\u003eEubacterium nodatum group\u003c/em\u003e exhibited a positive association with the risk of GCA, while KD demonstrated a positive association with the risk of the genus \u003cem\u003eEubacterium oxidoreducens group\u003c/em\u003e. Conversely, GPA was negatively associated with the risk of the \u003cem\u003egenus Eubacterium oxidoreducens group\u003c/em\u003e. The abundances of the family \u003cem\u003eLachnospiraceae\u003c/em\u003e and genus \u003cem\u003eLachnospira\u003c/em\u003e are negatively associated with the risk of GPA. The abundances of the genera \u003cem\u003eLachnospira\u003c/em\u003e and \u003cem\u003eLachnospiraceae UCG004\u003c/em\u003e demonstrated positive associations with the risk of KD and GCA, respectively. Conversely, GPA was negatively associated with the risk of the genus \u003cem\u003eLachnospiraceae UCG004\u003c/em\u003e. BD and GPA both showed positive associations with the risk of the genus \u003cem\u003eHoldemanella\u003c/em\u003e. Additionally, the abundance of the genus \u003cem\u003eHoldemania\u003c/em\u003e was positively associated with the risk of GCA.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\" class=\"Section3\"\u003e\n\u003ch2\u003e3.4.2. The interaction of the gut microbiota with two types of immune-related vasculitis\u003c/h2\u003e\n\u003cp\u003e\u003cstrong\u003eBD and GCA.\u003c/strong\u003e BD was negatively associated with the risk of the phylum Verrucomicrobia, family Verrucomicrobiaceae, class Verrucomicrobiae, and order Verrucomicrobiales. Concurrently, the abundance of the phylum Verrucomicrobia demonstrated a negative association with the risk of GCA.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGCA and GPA.\u003c/strong\u003e GPA was negatively associated with the risk of the genus \u003cem\u003eVeillonella\u003c/em\u003e, and the abundance of the genus \u003cem\u003eVeillonella\u003c/em\u003e was negatively associated with the risk of GCA. Simultaneously, the abundance of the family \u003cem\u003ePorphyromonadaceae\u003c/em\u003e showed a negative association with the risk of GCA and GPA.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBD and GPA.\u003c/strong\u003e The abundance of the class \u003cem\u003eMelainabacteria\u003c/em\u003e demonstrated a positive association with the risk of BD; conversely, it exhibited a negative association with the risk of GPA. Similarly, the abundance of the family \u003cem\u003eStreptococcaceae\u003c/em\u003e exhibited a negative association with the risk of GPA; conversely, BD showed a positive association with the risk of the family \u003cem\u003eStreptococcaceae\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGCA and KD.\u003c/strong\u003e GCA and KD were both negatively associated with the risk of the genus \u003cem\u003eAlistipes\u003c/em\u003e, and GCA was also negatively associated with the risk of the family \u003cem\u003eVictivallaceae\u003c/em\u003e. Conversely, the abundance of the genus \u003cem\u003eVictivallis\u003c/em\u003e was positively associated with the risk of KD.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eKD and GPA.\u003c/strong\u003e Both KD and GPA were negatively associated with the risk of the genus \u003cem\u003eEubacterium eligens group\u003c/em\u003e, with KD also exhibiting negative associations with the risk of the order \u003cem\u003eActinomycetales\u003c/em\u003e, family \u003cem\u003eActinomycetaceae\u003c/em\u003e, genus \u003cem\u003eActinomyces\u003c/em\u003e and genus \u003cem\u003eButyricimonas\u003c/em\u003e. In contrast, the abundances of the family \u003cem\u003eActinomycetaceae\u003c/em\u003e and genus \u003cem\u003eButyricimonas\u003c/em\u003e were positively associated with the risk of GPA.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBD and KD.\u003c/strong\u003e BD was positively associated with the risk of the order \u003cem\u003eLactobacillales\u003c/em\u003e and the genus \u003cem\u003eStreptococcus\u003c/em\u003e. Conversely, KD was negatively associated with the risk of the genus \u003cem\u003eStreptococcus\u003c/em\u003e. BD is also negatively associated with the risk of the genus \u003cem\u003eRuminiclostridium 6\u003c/em\u003e. The abundances of the genera \u003cem\u003eLactobacillus\u003c/em\u003e and \u003cem\u003eRuminiclostridium 9\u003c/em\u003e were negatively associated with the risk of KD.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis MR study represents the first examination of the potential causal association between the gut microbiota and immune-related vasculitis, which we expect will serve as a foundation for future longitudinal investigations of alterations in the microbiome preceding the onset of immune-related vasculitis. Unexpectedly, genetic predispositions to colonization with the class \u003cem\u003eMelainabacteria\u003c/em\u003e, class \u003cem\u003eLentisphaeria\u003c/em\u003e, and family \u003cem\u003eActinomycetaceae\u003c/em\u003e demonstrated causal links with GPA. Additionally, we identified specific gut microbiota constituents that could serve as potential risk factors for immune-related vasculitis. These findings will support public health interventions aimed at mitigating the risk of immune-related vasculitis.\u003c/p\u003e \u003cp\u003eGCA, characterized by granulomatous inflammation in large and medium-sized vessels, primarily affects elderly patients [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. No specific investigation has been dedicated to exploring the gut microbiome in GCA patients. However, studies focusing on the microbiome in the blood and aorta have highlighted distinctions in GCA patients. These distinctions include a decrease in \u003cem\u003eActinobacteria\u003c/em\u003e abundance and an increase in \u003cem\u003eProteobacteria\u003c/em\u003e abundance and minimal \u003cem\u003eBacteroidetes\u003c/em\u003e abundance compared to those in individuals with noninflammatory thoracic aortic aneurysms. Moreover, in contrast to patients with non-GCA temporal arteritis, GCA patients had variable proportions of \u003cem\u003eProteobacteria\u003c/em\u003e, \u003cem\u003eBifidobacterium\u003c/em\u003e, \u003cem\u003eParasutterella\u003c/em\u003e, and \u003cem\u003eGranulicatella\u003c/em\u003e. Notably, there was variation in the abundance of \u003cem\u003eRhodococcus\u003c/em\u003e, an unidentified member of the family \u003cem\u003eCytophagaceae\u003c/em\u003e, in blood samples. Additionally, GCA patients exhibit dissimilarities in microbiome composition between the temporal artery and thoracic artery [\u003cspan additionalcitationids=\"CR13 CR14\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Our study first revealed a negative association between the abundance of the genus \u003cem\u003eRuminococcaceae NK4A214 group\u003c/em\u003e and the risk of GCA. \u003cem\u003eRuminococcaceae NK4A214 group\u003c/em\u003e is linked to fiber degradation [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Additionally, research has indicated its role in advancing type 2 diabetes in a Mexican cohort [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Moreover, the abundance of \u003cem\u003eRuminococcaceae NK4A214 group\u003c/em\u003e effectively differentiated between chronic kidney disease patients and healthy controls [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Moreover, our study identified a complex interaction network between immune-related vasculitis and \u003cem\u003eRuminococcaceae\u003c/em\u003e spp., suggesting the potential relevance of this genus to the onset and progression of immune-related vasculitis. Additionally, we identified other gut microbiota constituents, including the genera \u003cem\u003eVeillonella\u003c/em\u003e, \u003cem\u003eRuminococcus 2\u003c/em\u003e, \u003cem\u003eFlavonifractor\u003c/em\u003e, and \u003cem\u003eRuminococcus gnavus\u003c/em\u003e, as potential risk factors for GCA.\u003c/p\u003e \u003cp\u003eBD is an uncommon form of vasculitis, its etiology remains elusive, and it involves multiple organs. The disease is characterized by mucocutaneous symptoms such as oral and genital aphthosis and aseptic folliculitis. Additionally, patients may experience ocular complications such as uveitis, vascular issues leading to thrombosis, and further manifestations in the articular, gastrointestinal, and neurological systems [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Research on the role of the gut microbiome in BD patients surpasses that for other vasculitis types. 16S rRNA sequencing revealed that BD patients exhibit gut microbiota dysbiosis, which impacts intestinal immune function and influences BD progression. These associations were initially highlighted in mouse models. Ongoing clinical trials are exploring microbial therapies for BD, though the efficacy of dietary interventions remains unclear. Cross-sectional analyses revealed distinct gut bacterial profiles in BD patients, which were characterized by elevated abundances of lactic acid and sulfate-producing bacteria but reduced abundances of lactic butyric acid producers and methanogens [\u003cspan additionalcitationids=\"CR17 CR18 CR19 CR20 CR21 CR22 CR23\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Notably, research has shown specific microbial shifts, such as increases in \u003cem\u003eTenericutes\u003c/em\u003e abundance and decreases in \u003cem\u003eDeferribacteres\u003c/em\u003e and \u003cem\u003eVerrucomicrobia\u003c/em\u003e abundance, in BD mice. Additionally, treatments with butyrate or \u003cem\u003eEubacterium rectale\u003c/em\u003e, a butyrate-producing bacterium, mitigated BD symptoms, suggesting therapeutic potential [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. According to reverse MR analysis, our study revealed that BD was positively associated with the risk of the family \u003cem\u003eStreptococcaceae\u003c/em\u003e. Initially, the role of \u003cem\u003eStreptococcus\u003c/em\u003e spp. in BD was investigated, given their prevalence in the oral bacterial community and association with oral diseases. Elevated abundances of distinct streptococcal serotypes have been observed in the oral mucosa of BD patients compared to those of healthy individuals [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. Further substantiating the pathogenic potential of \u003cem\u003eStreptococcus\u003c/em\u003e in BD, mouse studies revealed that introducing \u003cem\u003eStreptococcus sanguinis\u003c/em\u003e from BD patients led to the manifestation of BD-related symptoms [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. Therefore, the aforementioned preliminary findings align with our results. Our study delineated a multifaceted interaction network between \u003cem\u003eStreptococcus\u003c/em\u003e spp. and immune-related vasculitis, encompassing BD, KD, and GPA, indicating the potential involvement of these bacteria in the initiation and progression of these conditions. Furthermore, we identified additional gut microbiota constituents, such as the classes \u003cem\u003eMelainabacteria\u003c/em\u003e and \u003cem\u003eGammaproteobacteria\u003c/em\u003e, family \u003cem\u003eRhodospirillaceae\u003c/em\u003e, and genera \u003cem\u003eOdoribacter\u003c/em\u003e and \u003cem\u003eRuminococcaceae UCG011\u003c/em\u003e, that may serve as potential risk factors for BD.\u003c/p\u003e \u003cp\u003eKD is severe systemic vasculitis frequently associated with coronary artery aneurysms [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. The susceptibility of the intestinal microbiome to environmental factors is believed to influence KD development [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. Associations with KD have been identified for \u003cem\u003eBacteroidetes\u003c/em\u003e and \u003cem\u003eDorea\u003c/em\u003e [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e], and \u003cem\u003eFusobacteria\u003c/em\u003e, \u003cem\u003eShigella\u003c/em\u003e, and \u003cem\u003eStreptococcus\u003c/em\u003e have also been suggested as potential influencing factors [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. While the \u003cem\u003eRuminococcus\u003c/em\u003e abundance increases during the nonacute stages of KD, \u003cem\u003eStreptococcus\u003c/em\u003e becomes more enriched in the acute phases [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. A comparison of microbial sequences from throat, rectum, and blood samples highlighted similarities between the blood and gut microbiota [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. In a murine model of exposure to a \u003cem\u003eLactobacillus\u003c/em\u003e cell wall extract, both bacteria and fungi were demonstrated to influence KD progression and severity [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. Antibiotic administration has also been associated with KD development [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. After treatment with immunoglobulin/antibiotics, a decrease in the abundances of harmful bacteria and an increase in the abundances of beneficial bacteria have been observed [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. This microbial shift could enhance intestinal permeability, allowing intestinal pathogens to induce irregular immune reactions. Our study revealed that the abundance of the genus \u003cem\u003eLactobacillus\u003c/em\u003e was potentially negatively associated with the risk of KD. Moreover, KD was potentially negatively associated with the risk of the family \u003cem\u003eRuminococcaceae\u003c/em\u003e, genus \u003cem\u003eRuminococcus torques group\u003c/em\u003e, genus \u003cem\u003eRuminococcus 2\u003c/em\u003e, and genus \u003cem\u003eStreptococcus\u003c/em\u003e. These findings diverge from those of the aforementioned observational studies, which we primarily attribute to the limited sample size of KD patients in our MR analysis. Consequently, the conclusions drawn regarding the causal association between KD and the gut microbiota should be interpreted as indicative rather than definitive, warranting further investigation in subsequent research.\u003c/p\u003e \u003cp\u003eGPA is a rare necrotizing vasculitis primarily affecting the upper and lower respiratory tracts and the renal system and involving small to medium-sized vessels [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Recent research has elucidated the association between GPA and the nasal microbiome. Rhee et al. observed fluctuations in the nasal microbiota of GPA patients, particularly in the \u003cem\u003eCorynebacterium\u003c/em\u003e-to-\u003cem\u003eStaphylococcus\u003c/em\u003e ratio, with distinct changes preceding disease relapses [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. Using deep sequencing, another group discerned a microbial imbalance in GPA patients at the bacterial and fungal tiers, with immunosuppressive therapy associated with a more normalized profile [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]. Wagner et al. identified enrichment of \u003cem\u003eStaphylococcus aureus\u003c/em\u003e in patients with active GPA, contrasting with the prevalence of \u003cem\u003eStaphylococcus epidermidis\u003c/em\u003e in patients with inactive GPA [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e]. Furthermore, Lamprecht et al. observed reduced microbiome diversity in GPA patients and revealed enhanced colonization of \u003cem\u003eStaphylococcus aureus\u003c/em\u003e, along with pathogens such as \u003cem\u003eHaemophilus influenzae\u003c/em\u003e and rhinoviruses, emphasizing the microbial dysbiosis [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e]. Together, these findings underscore the nuanced interplay between GPA and the nasal microbiome, emphasizing the potential roles of specific bacterial genera. Further investigations are essential to clarify the underlying mechanisms and possible therapeutic approaches. Niccolai et al. investigated the gut microbiota in eosinophilic granulomatosis with polyangiitis patients and identified an increase in the abundance of potential pathobionts, specifically \u003cem\u003eEnterobacteriaceae\u003c/em\u003e and \u003cem\u003eStreptococcaceae\u003c/em\u003e, during active disease [\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e]. Concurrently, Yu et al. noted increased \u003cem\u003eActinomyces\u003c/em\u003e and \u003cem\u003eStreptococcus\u003c/em\u003e abundances and reduced abundances of SCFA-producing taxa in microscopic polyangiitis patients [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e]. To date, no published study has explored the association between GPA and the gut microbiota. The present MR investigation was conducted based on the most recent GWAS datasets for GPA. The GWAS dataset for GPA included 135 cases and 456,213 controls from the publicly available GWAS catalog. Consequently, our study revealed that the abundances of the families \u003cem\u003eClostridiaceae 1\u003c/em\u003e and \u003cem\u003eActinomycetaceae\u003c/em\u003e were positively associated with the risk of GPA. Moreover, the abundances of the classes \u003cem\u003eLentisphaeria\u003c/em\u003e, \u003cem\u003eMelainabacteria\u003c/em\u003e, and \u003cem\u003eNegativicutes\u003c/em\u003e and the families \u003cem\u003eLachnospiraceae\u003c/em\u003e, \u003cem\u003ePorphyromonadaceae\u003c/em\u003e, \u003cem\u003eRuminococcaceae\u003c/em\u003e, and \u003cem\u003eStreptococcaceae\u003c/em\u003e were negatively associated with the risk of GPA. This study represents the first exploration of the causal association between GPA and the gut microbiota on the bases of a comprehensive GWAS sample, yielding robust and credible findings. Through this research, we identified a shared gut microbiota signature, notably involving the \u003cem\u003eStreptococcaceae\u003c/em\u003e family, between GPA and other AAVs.\u003c/p\u003e \u003cp\u003eThe relationship between the gut microbiota and immune-related vasculitis highlights the potential regulatory role of the gut microbiome in these diseases. The overlap of gut microbiota patterns among diverse vasculitides indicates that shared underlying mechanisms are influenced by the gut microbial composition, suggesting potential therapeutic targets applicable to multiple vasculitis conditions. Specifically, the family \u003cem\u003eRuminococcaceae\u003c/em\u003e exhibits distinct associations across diseases (e.g., negative association with GPA but positive association with BD and GCA), underscoring the importance of a detailed understanding of microbial taxa rather than generalized interpretations. Associations of \u003cem\u003eEubacterium\u003c/em\u003e and \u003cem\u003eLachnospiraceae\u003c/em\u003e emphasize their relevance in vasculitis pathways (e.g., \u003cem\u003eEubacterium nodatum group\u003c/em\u003e with GCA), with certain genera playing possible proinflammatory roles. Conversely, the negative associations of \u003cem\u003eVerrucomicrobia\u003c/em\u003e and \u003cem\u003eVeillonella\u003c/em\u003e with GCA suggest potential protective roles or altered states in disease contexts. The bidirectional relationships within the microbial ecosystem, exemplified by \u003cem\u003eActinomycetales\u003c/em\u003e and \u003cem\u003eActinomycetaceae\u003c/em\u003e (which are negatively associated with KD and GPA but positively associated with other diseases), emphasize their context-specific roles in different diseases. Moreover, the positive associations of BD with \u003cem\u003eLactobacillales\u003c/em\u003e and \u003cem\u003eStreptococcus\u003c/em\u003e contrast with their negative associations with KD, emphasizing the importance of considering broader microbial interactions. In summary, while specific microbial patterns correlate with distinct diseases, necessitating further exploration into their causative, functional, and therapeutic implications in vasculitis.\u003c/p\u003e \u003cp\u003eThe current study has several distinct strengths. First, this study provides the first application of a 2-sample bidirectional MR approach, probing the causal ties between the gut microbiota and immune-related vasculitis subtypes, namely, GCA, BD, KD, and GPA. This methodology diminishes issues such as reverse causation and confounding effects, which are often prevalent in observational studies. To enhance the robustness of our findings, we integrated multiple MR framework strategies, ensuring consistency in the results both pre- and postoutlier adjustments while also minimizing variability. For comprehensive genetic insights, we utilized an expansive GWAS dataset at the summary level. The observed disparities between exposure and outcome data further validate our conclusions.\u003c/p\u003e \u003cp\u003eNevertheless, several limitations were evident in this research. Initially, the limited number of SNPs employed as IVs reduced overall statistical robustness. However, given that our F-statistics consistently surpassed 10, the risk of significant instrumental bias in our conclusions remained minimal. underscoring the necessity for larger sample sizes in subsequent MR investigations. Second, the predominant European ancestry of our participants prohibits direct generalization to diverse racial or ethnic populations, emphasizing the need for replicative studies. Finally, our analysis focused solely on bacterial taxa at the genus level, not achieving more granular classifications such as species or strains. Utilizing advanced shotgun metagenomic techniques in microbiota GWASs could increase the precision of the results.\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eIn summary, our results substantiate the causal roles of the class \u003cem\u003eMelainabacteria\u003c/em\u003e, \u003cem\u003eLentisphaeria\u003c/em\u003e and family \u003cem\u003eActinomycetaceae\u003c/em\u003e in GPA. Moreover, we identified distinct gut microbiota elements that might act as potential triggers for immune-related vasculitis. This research offers fresh perspectives on the mechanisms underlying the progression of gut microbiota-associated immune-related vasculitis.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by funding from STI2030-Major Projects (2021ZD0200600, 2021ZD0200603), the National Key Research and Development Program (2022YFC2009600) (2022YFC2009602), the \u0026ldquo;Beijing Major Epidemic Prevention and Control Key Specialty Construction Project\u0026rdquo; (2022).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYuan H, Zeng X, and Chen S conceptualized and designed the study; Chen S retrieved the data; Chen S analyzed, interpreted, and drafted the article; Nie R, Wang C, Luan H, Xu M, and Gui Y revised the article; Chen S, Nie R, and Wang C generated the graphs and tables. All the authors approved the final version for submission.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study utilized publicly available datasets, which were obtained from the MiBioGen database (https://mibiogen.gcc.rug.nl/), the GWAS catalog (https://www.ebi.ac.uk/gwas/), the IEU OpenGWAS database (https://gwas.mrcieu.ac.uk/) and the FinnGen consortium (https://www.finngen.fi/).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe appreciated all the genetics consortiums for making the GWAS summary data publicly available.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eJennette, JC. 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Previous antibiotic use and the development of Kawasaki disease: a matched pair case-control study. \u003cem\u003ePediatr Int\u003c/em\u003e 62:1044-1048. https://doi.org/10.1111/ped.14255 (2020)\u003c/li\u003e\n\u003cli\u003eRhee, RL, Lu, J, Bittinger, K\u003cem\u003e et al\u003c/em\u003e. Dynamic Changes in the Nasal Microbiome Associated With Disease Activity in Patients With Granulomatosis With Polyangiitis. \u003cem\u003eArthritis Rheumatol\u003c/em\u003e 73:1703-1712. https://doi.org/10.1002/art.41723 (2021)\u003c/li\u003e\n\u003cli\u003eRhee, RL, Sreih, AG, Najem, CE\u003cem\u003e et al\u003c/em\u003e. Characterisation of the nasal microbiota in granulomatosis with polyangiitis. \u003cem\u003eAnn Rheum Dis\u003c/em\u003e 77:1448-1453. https://doi.org/10.1136/annrheumdis-2018-213645 (2018)\u003c/li\u003e\n\u003cli\u003eWagner, J, Harrison, EM, Martinez Del Pero, M\u003cem\u003e et al\u003c/em\u003e. The composition and functional protein subsystems of the human nasal microbiome in granulomatosis with polyangiitis: a pilot study. \u003cem\u003eMicrobiome\u003c/em\u003e 7:137. https://doi.org/10.1186/s40168-019-0753-z (2019)\u003c/li\u003e\n\u003cli\u003eLamprecht, P, Fischer, N, Huang, J\u003cem\u003e et al\u003c/em\u003e. Changes in the composition of the upper respiratory tract microbial community in granulomatosis with polyangiitis. \u003cem\u003eJ Autoimmun\u003c/em\u003e 97:29-39. https://doi.org/10.1016/j.jaut.2018.10.005 (2019)\u003c/li\u003e\n\u003cli\u003eNiccolai, E, Bettiol, A, Baldi, S\u003cem\u003e et al\u003c/em\u003e. Gut Microbiota and Associated Mucosal Immune Response in Eosinophilic Granulomatosis with Polyangiitis (EGPA). \u003cem\u003eBiomedicines\u003c/em\u003e 10:https://doi.org/10.3390/biomedicines10061227 (2022)\u003c/li\u003e\n\u003cli\u003eYu, B, Jin, L, Chen, Z\u003cem\u003e et al\u003c/em\u003e. The gut microbiome in microscopic polyangiitis with kidney involvement: common and unique alterations, clinical association and values for disease diagnosis and outcome prediction. \u003cem\u003eAnn Transl Med\u003c/em\u003e 9:1286. https://doi.org/10.21037/atm-21-1315 (2021)\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"gut microbiota, immune-related vasculitis, mendelian randomization, GPA, SNPs","lastPublishedDoi":"10.21203/rs.3.rs-3874319/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3874319/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eObservational data suggest a link between gut microbiota and immune-related vasculitis, but causality remains unclear. A bidirectional mendelian randomization (MR) study was conducted using public genome-wide data. The inverse-variance-weighted (IVW) method identified associations and addressed heterogeneity. Families \u003cem\u003eClostridiaceae 1\u003c/em\u003e and \u003cem\u003eActinomycetaceae\u003c/em\u003e correlated positively with granulomatosis with polyangiitis risk, while classes \u003cem\u003eLentisphaeria\u003c/em\u003e and \u003cem\u003eMelainabacteria\u003c/em\u003e, and families \u003cem\u003eLachnospiraceae\u003c/em\u003e and \u003cem\u003eStreptococcaceae\u003c/em\u003e showed negative associations. Beh\u0026ccedil;et's disease was positively associated with the risk of family \u003cem\u003eStreptococcaceae\u003c/em\u003e abundance. And other several gut microbiota constituents were identified as potential risk factors for immune-related vasculitis. Furthermore, combining positive association results from the IVW analysis revealed numerous shared gut microbiota constituents associated with immune-related vasculitis. MR analysis demonstrated a causal association between the gut microbiota and immune-related vasculitis, offering valuable insights for subsequent mechanistic and clinical investigations into microbiota-mediated immune-related vasculitis.\u003c/p\u003e","manuscriptTitle":"Causal effects of the gut microbiome on immune-related vasculitis: A two-sample Mendelian randomization study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-25 11:48:18","doi":"10.21203/rs.3.rs-3874319/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-07-03T08:38:33+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-06-25T20:17:10+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"87049550386223772957069182721543907733","date":"2024-06-11T14:08:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"177daf8c-9814-491b-81bb-c8a51d8a0b35","date":"2024-04-18T11:13:42+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-03-10T13:49:38+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"d4a02e2e-68a9-4478-bdcd-be1d2f5d5c2b","date":"2024-03-05T23:06:27+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"7fd0e196-0bf4-47c8-bf4d-a51ac9d7c37b","date":"2024-03-04T17:53:54+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-02-09T15:08:36+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-02-05T04:33:10+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-01-23T05:28:16+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-01-23T05:25:12+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-01-18T01:43:31+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"17d32098-b0e5-46ef-a41f-98bf37a148ba","owner":[],"postedDate":"January 25th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":28306265,"name":"Biological sciences/Immunology/Autoimmunity"},{"id":28306266,"name":"Biological sciences/Microbiology/Microbial genetics/Bacterial genetics"}],"tags":[],"updatedAt":"2024-08-22T19:34:47+00:00","versionOfRecord":{"articleIdentity":"rs-3874319","link":"https://doi.org/10.1038/s41598-024-68205-0","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2024-08-13 15:58:11","publishedOnDateReadable":"August 13th, 2024"},"versionCreatedAt":"2024-01-25 11:48:18","video":"","vorDoi":"10.1038/s41598-024-68205-0","vorDoiUrl":"https://doi.org/10.1038/s41598-024-68205-0","workflowStages":[]},"version":"v1","identity":"rs-3874319","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3874319","identity":"rs-3874319","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}