Two-sample Mendelian randomization analyses support cross-talk between air pollution exposure and gut microbiome

Two-sample Mendelian randomization analyses support cross-talk between air pollution exposure and gut microbiome | 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 Two-sample Mendelian randomization analyses support cross-talk between air pollution exposure and gut microbiome Shaowei Gu, Yikun Cui, Hui Chen, Hao Bai, Xiaolin Yin, Xiaorong Yang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5837896/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Various studies have suggested the intriguing potential of air pollution exposure to influence gut microbiota diversity. It can impact gut microbiota not only by directly entering the intestine, but also through the gut-lung axis when deposited in lungs. Nevertheless, the scarcity of compelling genetic causal evidence remains conspicuous. Our objective was to evaluate whether a genetic causal relationship exists between air pollution and gut microbiota, along with the potential implications of this connection. Method This study was designed to investigate the link between air pollutant exposure (encompassing PM 2.5 , PM 10 , PM 2.5−10 , NO 2 , and NO x ) and alterations in the gut microbiome using a two-sample Mendelian randomization method based on summary-level GWAS study. To explore the effect of air pollutants on gut microbiota, we conducted MR analyses across five specific feature levels, including phylum, class, order, family, and genus. The main analytical approach employed was inverse variance weighting (IVW), which examined the relationship between exposure and outcome by assessing single nucleotide polymorphisms (SNPs) linked to air pollution.. Additional sensitivity analyses, such as Cochran Q test, MR-Egger regression, and leave-one-out analysis, were conducted to evaluate the robustness of the findings. Results A statistically noteworthy association was observed between NO 2 exposure and an uptick in the genus Eubacterium fissicatena group [IVW-odds ratio ( OR ) = 2.20; 95% confidence interval ( CI ), 1.42–3.41; P = 4.36*10 − 4 ], the Gordonibacter genus (IVW- OR = 2.29; 95%CI: 1.48–3.56; P = 2.17*10 − 4 ), and the LachnosPiraceae genus (IVW-OR = 1.82; 95%CI: 1.32–2.51; P = 2.37*10 − 4 ). Contrarily, a decrease in the abundance of the Holdemania genus (IVW-OR = 0.616; 95%CI: 0.47–0.81; P = 6.58*10 − 4 ) and the Ruminococcus gauvreauii genus (IVW-OR = 0.663; 95%CI: 0.53–0.83; P = 4.63*10 − 4 ) was linked with NO 2 exposure. Furthermore, PM 2.5 exposure was associated with a lower presence of Family XIII (IVW-OR = 0.691; 95%CI: 0.55–0.87; P = 1.47*10 − 3 ). Conclusion Our findings indicate air pollutants, particularly NO 2 and PM 2.5 , appeared to have a noteworthy association with the gut microbiota's composition, especially for genus Eubacterium fissicatena group, Gordonibacter genus, LachnosPiraceae genus, Holdemania genus and the Ruminococcus gauvreauii genus. This may offer valuable insights for further investigations into the mechanisms and clinical implications of air pollution-induced dysbiosis of the gut microbiome. Earth and environmental sciences/Environmental sciences/Environmental impact Health sciences/Risk factors Air pollution Gut microbiome Mendelian randomization Particulate matter Nitrogen oxide Figures Figure 1 Figure 2 Figure 3 Introduction Air pollutants typically include a spectrum of different sizes of particulate matter (PM), nitrogen oxides, and numerous other harmful components. In recent years, there has been growing attention to the association between air pollution exposure and human health. Studies have shown that particulate matter air pollution surpassed high systolic blood pressure, smoking, low birth weight and preterm birth, as well as high fasting blood glucose levels in contributing to the global disease burden in 2021[ 1 ]. The mechanisms underlying how air pollution plays negative roles in health are not fully understood, but one of the hypotheses concerns its influence on the gut microbiome[ 2 ]. Disruptions in the gut microbiota can affect human health and are associated with conditions such as irritable bowel syndrome (IBS), liver disease and inflammatory bowel disease[ 3 , 4 ]. Research suggests that the microbial makeup in individuals with IBS deviates from that of healthy counterparts, marked by a decrease in beneficial bacteria and a rise in harmful ones. Specifically, changes in the levels of bacteria like the genus Ruminococcus gauvreauii group have been strongly correlated with the onset of IBS. And the severity and duration of IBS symptoms have been associated with disruptions in gut microbiota balance. Meanwhile, the dynamic equilibrium of the gut microbiome can be disrupted by a range of factors, including genetics, aging, lifestyle choices, and environmental conditions[ 5 , 6 ]. Research indicates that air pollutants can negatively impact the gastrointestinal system[ 7 , 8 ] and chronic exposure to PM 2.5 and its components have been linked to gut microbiota dysbiosis in both adults and children[ 9 , 10 ]. There's also emerging evidence suggesting that air pollutants can affect the relative population of gut bacteria[ 11 , 12 ]. Thus far, there have been few studies conducted on human that explore the relationship between exposure to air contaminants and the diversity and functional capacity of the gut microbiome[ 13 ]. Recently, the MiBioGen consortium unveiled numerous genetic loci associated with variations in microbiome abundance, creating an unparalleled opportunity to probe the potential cause-and-effect relationship connecting the gut microbiota and air pollutants. Mendelian randomization (MR) is a suitable tool to evaluate the genuine causal association between exposure and outcomes by capitalizing on genetic variants. One of the advantages of MR is its ability to overcome confounding and reverse causation, which are common limitations in observational studies. Since genetic variants are determined at conception and remain fixed throughout life, they are not influenced by environmental or behavioral factors that may confound observational associations. This helps establish temporality, ensuring that the exposure precedes the outcome, thus strengthening the inference of causality. Moreover, MR can provide more reliable estimates of causal effects compared to observational studies by mimicking the random allocation of treatments in a controlled trial. Therefore, Mendelian randomization is increasingly being applied. For instance, some scholars have used the Mendelian randomization method to study the relationship between gut diseases and mental disorders, achieving significant progress[ 14 , 15 ].This allows for a more robust assessment of causal relationships, especially when randomized controlled trials are not feasible or ethical. Therefore, we proposed a potential correlation between air pollutants, specifically the residential exposures to PM 2.5 , PM 10 , NO 2 and NOx, and alterations in the gut microbiome, using an MR analysis to reveal the underlying causal connection. Methods 2.1 Study design Utilizing a two-sample MR approach, we evaluated the causal impact of PM 2.5 , PM 10 , PM 2.5~10 , NO 2 , and NO x on gut microbiota. To thoroughly explore the influence of air pollutants on gut microbiota, we conducted MR analyses across five specific feature levels, including phylum, class, order, family, and genus. The design of the study, along with the essential MR assumptions, is depicted in Figure 1. 2.2 Data sources The data for the exposures, namely PM 2.5 , PM 10 , PM 2.5~10 , NO 2 , and NO X , were collected from the summary datasets of GWAS from the UK Biobank, a substantial prospective study encompassing over half a million participants in UK[16]. Comprehensive procedures related to phenotyping, genetic specifics, genome-wide genotyping, imputation, and quality control of UK Biobank participants have been meticulously outlined in a separate report[17]. It should be noted that all participants gave their informed consent in the original studies from which the data were gathered. The GWAS summary dataset for PM 2.5 (identified as GWAS ID: ukb-b10817) encompassed 423,796 participants, all of whom were of European descent. The concentrations of PM 2.5 at the residences of the participants were determined using a land use regression (LUR) model as previously described[18]. The genetic data on gut microbiota were fetched from the most extensive genome-wide association study (GWAS) performed by the MiBioGen consortium, which included 18,340 participants from 24 cohorts[19]. The GWAS encompassed 211 taxa, broken down into 9 phyla, 16 classes, 20 orders, 36 families, and 131 genera. The microbial composition was identified by targeting three specific regions within the 16S rRNA gene. Additional details about the microbiota are available in the referenced study[19], and the GWAS data can be accessed at https://mibiogen.gcc.rug.nl/. 2.3 Selection of instrumental variables (IVs) As depicted in Figure 1, for the creation of valid IVs, genetic variations must adhere to three key assumptions of MR as follows: (1) The genetic IVs for air pollutants (such as PM 2.5 , PM 10 , PM 2.5~10 , NO 2 , and NO x ) must exhibit a significant correlation with levels of exposure to these pollutants. (2) The association between these genetic IVs and gut microbiota should be completely independent of any confounding factors. (3) The genetic IVs for environmental pollutants can only influence gut microbiota through exposure to environmental pollutants. Our research abides by the guidelines set forth in the Strengthening the Reporting of Observational Studies in Epidemiology using Mendelian Randomization (STROBE-MR) as established in a previous study[20]. We classified 211 bacterial taxa into five taxonomic ranks: phylum, class, order, family, and genus. However, 15 of these taxa remained unidentified, and we consequently excluded them from our investigation, leaving us with 196 bacterial taxa. We determined instrumental variables (IV) through a systematic approach. First, we identified all SNPs significantly associated with air pollution, applying a stringent threshold of genome-wide significance (P < 5×10 -8 ) to select our IVs. However, when extracting SNPs that robustly predicted the gut microbiome at this significance level, we found that only a limited number were available, resulting in insufficient SNPs for our analysis. Consequently, we opted for a more inclusive threshold (P < 1×10 -5 ) to gather the necessary SNPs for the two-sample Mendelian randomization analysis. [21, 22] . In line with prior research, we grouped the genetic variants located within 250 kb at a linkage disequilibrium (LD) threshold of r 2 <0.1. This was based on reference data of European ancestry from the 1,000 Genomes Project[23]. For each SNP, we calculated the F-statistic to measure the statistical power, discarding any that had an F-statistic below 10. The process for calculating the F-statistic has been elaborated on in other sources[22]. Lastly, through harmonization, we ensured the effect alleles of the exposure and outcome SNPs were aligned. We eliminated any SNPs that had inconsistent alleles (for example, A/C combined with A/G), or those that were palindromic with a moderate allele frequency. 2.4 Statistical analysis For univariable Mendelian Randomization (MR) analysis, we employed the inverse-variance weighted (IVW) method with random effects as the primary tool to infer causality. This involved combining Wald ratio estimates to yield a pooled effect on the outcome[24]. We considered the potential heterogeneity between Wald ratio estimates from SNPs[25]. Therefore, in the IVW method, heterogeneity was assessed by Cochran's Q' (modified) test. The odds ratio (OR) and 95% confidence interval (CI) represent the results. To test the resilience of the MR results, we utilized several alternative models, including the weighted median, MR-Egger, simple mode, and weighted mode methods[26, 27]. The weighted median method assumes that less than 50% of the SNPs are invalid. Its statistical power is slightly weaker than that of the IVW method[28]. The MR-Egger method, on the other hand, has relatively low power and is primarily employed for determining directionality[29]. To determine the statistical significance of the MR effect estimate, a false discovery rate (FDR) of less than 5% was used. For this purpose, we employed the Benjamini-Hochberg procedure, which is commonly used to correct multiple comparisons. This method adjusts the P-values to control the expected proportion of "discoveries" (rejected null hypotheses) that are false (incorrect rejections). By using this approach, we could account for the number of taxa tested and effectively manage the risk of false positives due to multiple tests. A two-sided P FDR value of < 0.05 was statistically significant, while P values less than 0.05 and P FDR values more than 0.05 were interpreted as indicating a suggested causal relationship. To scrutinize both substantial and minor causal relationships, we further implemented a set of sensitivity analyses. These analyses allowed us to discern any potential breaches of the MR principles. The 'leave one out' (LOO) approach was employed to assess whether a single outlier unduly influenced the overall calculation[30]. And we applied the Cochran Q test to examine the presence of heterogeneity[31]. Furthermore, to identify instances of horizontal pleiotropy, we utilized the intercept term acquired from the MR-Egger regression[26].Through this intercept, researchers can assess whether certain SNPs influence the outcome through pathways other than the exposure, thereby accounting for confounding effects and detecting the presence of pleiotropy. The entirety of these analyses was executed in the R programming environment (version 4.3.0), utilizing the "TwoSampleMR" package (version 0.5.7). Results In order to comprehend the effects of various air pollutants on the gut microbiota, we conducted separate analyses for distinct air pollutants (NO 2 , NO X , PM 2.5 , PM 10 , PM 2.5-10 ). In the comprehensive MR analysis, we assessed the associations between air pollutants and a total of 196 bacterial taxa encompassing 9 phyla, 16 classes, 20 orders, 32 families, and 119 genera (Table S1). After meticulous instrument selection processes, the count of SNPs linked with each bacterial taxon varied from a minimum of 3 to a maximum of 117. We assessed the potential bias of weak instrumental variables, and with every F-statistic surpassing 10, the results confirm that no weak IVs were included in our study. Figure 2 displays the initial correlations between five air pollutants (NO2, NOX, PM2.5, PM10, PM2.5-10) and bacterial taxa across various taxonomic levels, derived through the application of the Inverse Variance Weighted (IVW) method. The Effect of NO 2 on Intestinal Microbiota We detected five meaningful associations linked to NO 2 (Table 1). The genetic predisposition to NO 2 exposure significantly correlated with an elevated abundance of the Eubacterium fissicatena group genus (IVW-OR=2.20; 95%CI: 1.42–3.41; P =4.36*10 −4 ; P FDR =0.0227). To perform heterogeneity testing on NO 2 and the genus Eubacterium fissicatena group , the IVW method yielded Q pval =0.846, and the MR Egger method yielded Q pval =0.836. The P-value for the pleiotropy test was 0.525, all of which exceeded 0.05. The Gordonibacter genus (IVW-OR = 2.29; 95%CI: 1.48-3.56, P =2.17´10 -4 , P FDR =0.0227) also presented an increase in microbial abundance. Using the IVW method (Q pval =0.804) and the MR Egger method (Q pval =0.790) did not observe significant heterogeneity. The P-value for pleiotropy test was 0.620. Furthermore, the abundance of the LachnosPiraceae genus (IVW-OR=1.82; 95%CI: 1.32-2.51; P =2.37*10 -4 , P FDR =0.0227) also increased. No significant heterogeneity was found with the IVW method yielding Q pval =0.490 and the MR Egger method Q pval =0.492, moreover a pleiotropy test P-value of 0.302. Conversely, a lower abundance of the Holdemania genus (IVW-OR=0.62; 95%CI: 0.47-0.81; P =6.58*10 -4 , P FDR =0.0258). Heterogeneity testing was performed on the results, with the IVW method yielding Q pval =0.546 and the MR Egger method Q pval =0.539. The pleiotropy test of the results yielded a P-value of 0.393. Additionally, the Ruminococcus gauvreauii genus (IVW-OR=0.66; 95%CI: 0.53-0.83; P=4.63*10 -4 , P FDR =0.0227) was observed decreases in relation to genetically predicted NO 2 exposure. Conducting heterogeneity analysis, we obtained Q pval =721 with the IVW method and 0.702 with the MR Egger method. Furthermore, when assessing the pleiotropy test, we found a P-value of 0.722. The consistent direction and magnitude of estimates from other MR models like Weighted median, Simple mode, Weighted mode, and MR-Egger regression bolstered the causal interpretations (Table 2). The Cochran Q test did not reveal any heterogeneity (Table S1). Furthermore, the MR-Egger intercept analysis indicated an absence of potential horizontal pleiotropy (Table S1). The LOO analysis affirmed that no single SNP was driving these causalities. The Effect of PM 2.5 on Intestinal Microbiota Our observations indicated that a genetic predisposition to PM 2.5 exposure correlated with a decreased presence of Family XIII (IVW-OR=0.69 95%CI 0.55-0.87, P =1.47*10 -3 , P FDR =0.0471) (Table 1). Regarding this result, heterogeneity testing was conducted using the IVW method (Q pval =0.432) and the MR Egger method (Q pval =0.403). The pleiotropy test result yielded a P-value of 0.924. The causal associations were further substantiated by the consistent direction and magnitude of estimates from various other MR models. The study revealed no signs of heterogeneity or potential horizontal pleiotropy. Furthermore, the LOO analysis verified that these causalities were not influenced by any individual SNP. The other pollutants A lower abundance of the order NB1n was observed (IVW-OR=0.25; 95%CI: 0.11-0.59; P =1.52*10 -3 , P FDR =0.0304). Heterogeneity testing was conducted, with the IVW method yielding Q pval =0.766 and the MR Egger method Q pval =0.720. The pleiotropy test of the results yielded a P-value of 0.712. No associations were found between genetically predicted exposures to PM 10 , PM 2.5-10 , and NO x and the abundance of gut microbiota (Figure 2). It is noteworthy that some results showed P 0.05, indicating a potential association between the two that warrants further invest. The heterogeneity test for the genus Holdemania showed Q MR-Egger p = 0.015 and Q IVW p = 0.005, with pleiotropy p = 0.021. These results suggest potential pleiotropy, which contradicts the Mendelian assumption. As a result, the findings may not hold significant relevance, and further investigation is needed to explore how other factors may influence the gut microbiota associated with this genus. DISCUSSION In the course of our study, we have identified 5 gut microbiota genus that are impacted by air pollution. Specifically, 4 of them are influenced by NO 2 , including the Eubacterium fissicatena group , Gordonibacter , LachnosPiraceae , Holdemania and Ruminococcus gauvreauii genus, and Family XIII is affected by PM2.5. Given that established factors like diet and antibiotic usage account for only 16% of the variability in gut microbiome composition among individuals[ 32 , 33 ], recent research has pivoted towards investigating the relationship between air pollution and the gut microbiome. There are divergent findings in studies examining the associations between air pollution exposure and indices of gut microbiome diversity, evenness, and richness. While the majority of the reviewed publications have indicated alterations in microbial phyla following exposure, it is crucial to acknowledge the significant disparities observed across various studies[ 34 – 37 ]. Apart from Verrucromicrobia , no other taxonomic groups exhibited consistent findings across multiple animal studies, potentially indicative of confounding variables inherent to animal experimentation[ 38 ]. Consequently, these studies can only be interpreted as evidence of a correlation between exposure to air pollution and a modified composition of the animal gut microbiome. Notably, it should be recognized that animal studies on air pollution involve the administration of high doses of toxic compounds within a relatively short timeframe, which diverges from the chronic, low-level exposure characteristic of human scenarios. The consistent constraint observed in the reviewed studies lies in the absence of comprehensive compositional analyses concerning the particulate matter and gases to which both mice and humans were subjected. Meanwhile, MR provides a novel approach to investigate the relationship between air pollution and gut microbiota. It can overcome the confounding and reverse causation typical in observational studies, thus providing more reliable estimates of causal effects, and also avoid biases inherent in animal studies, where short-term exposure to high doses of pollutants differs from the chronic, low-level exposure typical in human scenarios. Therefore, we decided to delve into the potential causal link between environmental contaminants (specifically PM 2.5 , PM 10 , PM 2.5-10 , NO 2 , and NO x ) and human gut microbiota leveraging the MR design. However, MR has limitations and potential biases. It assumes genetic variants are only associated with the outcome through the exposure of interest, but violations like pleiotropy can bias results. Additionally, MR needs large sample sizes and relies on assumptions about genetic instruments, measurement error, and population stratification, which can also introduce bias if not properly addressed. A decline in the abundance of the Ruminococcus gauvreauii group can impact acetate synthesis, leading to a reduction in acetate levels. This group of bacteria can generate acetate from pyruvate via the acetyl-CoA pathway[ 39 ]. Acetate represents one of the most prevalent SCFA in the peripheral circulation. Additionally, acetate has the ability to permeate the blood-brain barrier, influencing appetite reduction via a central regulatory mechanism[ 40 ]. Moreover, acetate has been found to attenuate allergic airway disease by boosting regulatory T cells (Tregs)[ 41 ]. An escalated abundance of the Eubacterium fissicatena group genus can induce a surge in butyrate production[ 39 ]. Butyrate not only significantly influences colonocyte proliferation and apoptosis but also modulates immune responses. An increase in butyrate production within the intestine can lead to the onset of irritable bowel syndrome[ 42 ]. An increased presence of the Eubacterium fissicatena group genus can enhance the transformation of acetate to butyrate, thereby leading to a rise in butyrate and a further decrease in acetate[ 39 ]. The Gordonibacter genus is part of the Actinobacteria Phylum , characterized by Gram-positive bacteria with a high guanine and cytosine content. Notably, the Gordonibacter genus is closely associated with the metabolism of polyphenols[ 43 ]. Holdemania , which ranks among the top 30 most abundant genera in the gut microbiome of individuals from affluent countries[ 44 ], is also frequently observed in the Midwestern Reference Panel (MWRP), a resource used for comparative gut microbiome studies[ 45 ]. On the other hand, the LachnosPiraceae , another significant group within the Firmicutes phylum, appears to react to the metabolic health status of the host. Elevated abundances of this group have been observed in patient groups exhibiting impaired glucose metabolism[ 46 ]. LachnosPiraceae has been recognized as SCFA producers that positively influence the intestinal barrier[ 47 ]. For the first time, we employed the MR methodologies to conduct a large-scale causal analysis linking long-term exposure to air pollution levels in the same population with the abundance of gut microbiota. This approach mitigated confounding factors and addressed the limitations associated with short exposure observation periods in animal experiments. In future research endeavors, it is recommended to measure the elemental, organic, biological/microbiological, ionic, and carbonaceous components of particulate matter. Additionally, the gaseous phase should be examined for common pollutants, along with the analysis of volatile organic compounds. These measurements will aid in establishing causal relationships between atmospheric components and the observed alterations in the gut microbiome. Furthermore, assessments should be conducted to evaluate how pollutants and inhaled microbes may transform within the lungs and esophagus during inhalation and subsequent ingestion. Conclusion In conclusion, our study provides evidence linking air pollutants, especially NO2 and PM2.5, to significant changes in the composition of the gut microbiota, impacting genera such as Eubacterium fissicatena group , Gordonibacter , LachnosPiraceae , Holdemania , and Ruminococcus gauvreauii . These findings underscore the potential health implications of air pollution on overall well-being through its influence on the gut microbiota. Moving forward, further research should focus on elucidating the mechanistic pathways driving these associations and exploring therapeutic strategies aimed at modulating the gut microbiota to mitigate the adverse effects of air pollution on human health. Additionally, investigating interventions like dietary adjustments, probiotics, or targeted therapies could help alleviate the detrimental impacts of air pollutants on the gut microbiome and overall health. These insights underscore the necessity for interdisciplinary collaborations integrating environmental science, microbiology, and clinical medicine to develop effective strategies for reducing the health risks associated with air pollution. Declarations Data accessibility statement The original data sources used to support our findings of this study are accessed from the public summary GWAS resources. All detailed research approaches for the two-sample Mendelian randomization are retrieved online from every GWAS consortia. Declaration of Competing Interest The authors declare no potential conflicts of interest. Acknowledgments The authors are grateful to the participants and investigators of the included public GWAS studies. Funding This research was funded by the National Natural Science Foundation of China (82103912), the Taishan Scholars Program of Shandong Province (tsqn202312328), and the Excellent Youth Innovation Team of Shandong Provincial Higher Education Institutions (2022KJ012). The funding bodies played no part in the study's conception, data collection, data analysis, data interpretation, or manuscript preparation. All authors have unrestricted access to the study data, with the final decision to submit the manuscript for publication resting with the corresponding author. Authors' contributions: Study design: Xiaorong Yang and Xiaolin Yin; Data collection: Shaowei Gu; Data analyses: Shaowei Gu, Yikun Cui, Hui Chen, Hao Bai; Results visualization: Shaowei Gu; Results interpretations: All authors; Manuscript writing: Shaowei Gu; Manuscript revising: All authors; All authors approved the reported manuscript. 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Haro C, Rangel-Zuniga OA, Alcala-Diaz JF, Gomez-Delgado F, Perez-Martinez P, Delgado-Lista J, Quintana-Navarro GM, Landa BB, Navas-Cortes JA, Tena-Sempere M et al : Intestinal Microbiota Is Influenced by Gender and Body Mass Index . PLoS One 2016, 11 (5):e0154090. Bajinka O, Tan Y, Abdelhalim KA, Ozdemir G, Qiu X: Extrinsic factors influencing gut microbes, the immediate consequences and restoring eubiosis . AMB Express 2020, 10 (1):130. Kish L, Hotte N, Kaplan GG, Vincent R, Tso R, Ganzle M, Rioux KP, Thiesen A, Barkema HW, Wine E et al : Environmental particulate matter induces murine intestinal inflammatory responses and alters the gut microbiome . PLoS One 2013, 8 (4):e62220. Fujimura KE, Demoor T, Rauch M, Faruqi AA, Jang S, Johnson CC, Boushey HA, Zoratti E, Ownby D, Lukacs NW et al : House dust exposure mediates gut microbiome Lactobacillus enrichment and airway immune defense against allergens and virus infection . Proc Natl Acad Sci U S A 2014, 111 (2):805-810. Salim SY, Jovel J, Wine E, Kaplan GG, Vincent R, Thiesen A, Barkema HW, Madsen KL: Exposure to ingested airborne pollutant particulate matter increases mucosal exposure to bacteria and induces early onset of inflammation in neonatal IL-10-deficient mice . Inflamm Bowel Dis 2014, 20 (7):1129-1138. Mutlu EA, Comba IY, Cho T, Engen PA, Yazici C, Soberanes S, Hamanaka RB, Nigdelioglu R, Meliton AY, Ghio AJ et al : Inhalational exposure to particulate matter air pollution alters the composition of the gut microbiome . Environ Pollut 2018, 240 :817-830. Franklin CL, Ericsson AC: Microbiota and reproducibility of rodent models . Lab Anim (NY) 2017, 46 (4):114-122. Koh A, De Vadder F, Kovatcheva-Datchary P, Backhed F: From Dietary Fiber to Host Physiology: Short-Chain Fatty Acids as Key Bacterial Metabolites . Cell 2016, 165 (6):1332-1345. Frost G, Sleeth ML, Sahuri-Arisoylu M, Lizarbe B, Cerdan S, Brody L, Anastasovska J, Ghourab S, Hankir M, Zhang S et al : The short-chain fatty acid acetate reduces appetite via a central homeostatic mechanism . Nat Commun 2014, 5 :3611. Thorburn AN, McKenzie CI, Shen S, Stanley D, Macia L, Mason LJ, Roberts LK, Wong CH, Shim R, Robert R et al : Evidence that asthma is a developmental origin disease influenced by maternal diet and bacterial metabolites . Nat Commun 2015, 6 :7320. Halmos EP, Christophersen CT, Bird AR, Shepherd SJ, Gibson PR, Muir JG: Diets that differ in their FODMAP content alter the colonic luminal microenvironment . Gut 2015, 64 (1):93-100. Tomas-Barberan FA, Selma MV, Espin JC: Interactions of gut microbiota with dietary polyphenols and consequences to human health . Curr Opin Clin Nutr Metab Care 2016, 19 (6):471-476. Arumugam M, Raes J, Pelletier E, Le Paslier D, Yamada T, Mende DR, Fernandes GR, Tap J, Bruls T, Batto JM et al : Enterotypes of the human gut microbiome . Nature 2011, 473 (7346):174-180. Chen J, Ryu E, Hathcock M, Ballman K, Chia N, Olson JE, Nelson H: Impact of demographics on human gut microbial diversity in a US Midwest population . PeerJ 2016, 4 :e1514. Lippert K, Kedenko L, Antonielli L, Kedenko I, Gemeier C, Leitner M, Kautzky-Willer A, Paulweber B, Hackl E: Gut microbiota dysbiosis associated with glucose metabolism disorders and the metabolic syndrome in older adults . Benef Microbes 2017, 8 (4):545-556. Schnorr SL, Candela M, Rampelli S, Centanni M, Consolandi C, Basaglia G, Turroni S, Biagi E, Peano C, Severgnini M et al : Gut microbiome of the Hadza hunter-gatherers . Nat Commun 2014, 5 :3654. Tables Table 1. Significant MR results of causal links between air pollutants and gut microbiome by using the IVW method. Exposure Outcome No. SNP OR (95%CI) P IVW P FDR P pleiotropy P Heterogeneity NO 2 Genus: Gordonibacter 108 2.29 (1.48- 3.56) 2.17´10 -4 0.0227 0.620 0.804 Genus: Lachnospiraceae UCG008 111 1.82 (1.32- 2.51) 2.37´10 -4 0.0227 0.302 0.490 Genus: Eubacterium fissicatena group 108 2.20 (1.42- 3.41) 4.36´10 -4 0.0227 0.525 0.846 Genus: Ruminococcus Gauvreauii group 116 0.66 (0.53-0.83) 4.62´10 -4 0.0227 0.722 0.722 Genus: Holdemania 113 0.62 (0.47-0.81) 6.58´10 -4 0.0258 0.393 0.546 Family: Peptococcaceae 114 1.54 (1.18-2.02) 1.75´10 -3 0.0571 0.590 0.202 Genus: Defluviitaleaceae UCG011 112 0.64 (0.47-0.85) 2.63´10 -3 0.0736 0.641 0.659 Family: Defluviitaleaceae 112 0.65 (0.49-0.88) 4.51´10 -3 0.108 0.594 0.587 Genus: Barnesiella 116 0.73 (0.58-0.91) 4.98´10 -3 0.108 0.929 0.424116 Genus: Bilophila 116 0.73 (0.58-0.92) 8.34´10 -3 0.164 0.396 0.922 Family: Bacteroidaceae 117 0.78 (0.64-0.95) 0.0130 0.205 0.907 0.364 Genus: Bacteroides 117 0.78 (0.64-0.95) 0.0130 0.205 0.907 0.364 Genus: Lactococcus 109 1.79 (1.13- 2.83) 0.0136 0.205 0.766 0.103 Genus: Lachnospiraceae UCG010 116 0.73 (0.57-0.94) 0.0152 0.213 0.0808 0.631 Genus: Eubacterium ventriosum group 116 0.78 (0.63-0.97) 0.0231 0.301 0.856 0.981 Family: Porphyromonadaceae 117 0.80 (0.65-0.97) 0.0273 0.332 0.300 0.235 Family: Veillonellaceae 117 1.26 (1.02-1.54) 0.0288 0.332 0.775 0.591 Genus: Ruminiclostridium6 116 0.77 (0.60-0.99) 0.042 0.425 0.693 0.0663 Family: Clostridiales vadin BB60 group 115 1.31 (1.01-1.70) 0.0403 0.438 0.800 0.734 Genus: Eubacterium coprostanoligenes Group 117 1.22 (1.00-1.49) 0.0449 0.0449 0.180 0.952 PM 2.5 Family: Family: XIII 89 0.69 (0.55-0.87) 1.47´10-3 0.0471 0.924 0.432 Genus: Defluviitaleaceae UCG011 88 0.66 (0.48-0.91) 0.0104 0.783 0.904 0.783 Family: Defluviitaleaceae 88 0.68 (0.49-0.93) 0.0171 0.203 0.854 0.752 Family: Peptostreptococcaceae 90 1.31 (1.05-1.65) 0.0190 0.203 0.0637 0.678 Genus: Tyzzerella3 88 0.63 (0.42- 0.95) 0.0256 0.783 0.491 0.689 Genus: Holdemania 88 0.66 (0.46-0.95) 0.0261 0.783 0.0205 0.00539 Genus: Lachnospiraceae NC2004 group 87 0.63 (0.42-0.95) 0.0263 0.783 0.238 0.118 PM 10 Genus: Ruminococcaceae UCG009 34 0.45 (0.26- 0.78) 4.96´10-3 0.567 0.0541 0.444 Genus: Eubacterium xylanophilum group 36 0.60 (0.39-0.91) 0.0164 0.567 0.501 0.418 Genus: Faecalibacterium 37 0.65 (0.46-0.93) 0.0189 0.567 0.814 0.828 Genus: Eubacterium ventriosum group 36 0.62 (0.42-0.93) 0.0192 0.567 0.572 0.413 Genus: Alloprevotella 18 0.25 (0.08-0.84) 0.0246 0.581 0.912 0.442 Genus: Parabacteroides 37 1.49 (1.04-2.14) 0.030 0.592 0.864 0.578 Phylum: Bacteroidetes 37 1.47 (1.02- 2.12) 0.0413 0.207 0.375 0.321 Class: Bacteroidia 37 1.46 (1.01- 2.12) 0.0450 0.407 0.416 0.277 Order: Bacteroidales 37 1.46 (1.01- 2.12) 0.0450 0.625 0.416 0.277 Family: Ruminococcaceae 37 0.68 (0.47-0.99) 0.0447 0.660 0.277 0.287 PM 2.5-10 Order: NB1n 21 0.25 (0.11-0.59) 1.52´10 -3 0.0304 0.712 0.766 Genus: Family XIII UCG001 22 1.91 (1.11- 3.30) 0.0204 0.826 0.816 0.364 Genus: Lachnospiraceae UCG010 23 1.80 (1.08- 3.01) 0.0236 0.826 0.283 0.891 Genus: Ruminococcus gauvreauii group 23 0.56 (0.34-0.93) 0.0238 0.826 0.707 0.781 Genus: Allisonella 18 3.70 (1.12-2.23) 0.0319 0.826 0.178 0.346 NO X Genus: Lachnospiraceae UCG001 112 1.42 (1.05- 1.93) 0.0212 0.846 0.0108 0.0190 Genus: Ruminococcaceae UCG013 114 0.79 (0.64-0.97) 0.0247 0.846 0.417 0.735 Genus: Lachnospiraceae UCG008 112 1.43 (1.04- 1.96) 0.0287 0.846 0.595 0.768 Genus: Ruminococcaceae UCG005 114 1.26 (1.01-1.57) 0.0374 0.846 0.805 0.273 Genus: Hungatella 109 1.53 (1.01- 2.32) 0.0464 0.846 0.318 0.547 Genus: Gordonibacter 109 1.55 (1.00-2.41) 0.0497 0.846 0.636 0.871 Genus: Holdemania 112 0.69 (0.52-0.91) 0.0973 0.846 0.0187 0.835 NO 2 ，nitrogen dioxide; PM,particulate matter; IVW, Inverse-variance weighted; OR, Odds ratio ;CI, Confidence interval Table 2 The Results of the other four Mendelian randomization methods. SNPs MR.Egger Weighted.median Simple mode Weighted mode Air pollutants Gut microbiota (n) OR(95％) p OR(95％) p OR(95％) p OR(95％) p NO 2 Genus Eubacterium fissicatena group 108 1.13 (0.14- 9.09) 0.907 2.42 (1.31- 4.46) 0.00475 3.34 (0.48-23.02) 0.223 3.25 (0.55-19.32) 0.198 Genus Gordonibacter 108 3.85 (0.48-31.15) 0.209 2.00 (1.08- 3.72) 0.0279 1.09 (0.18- 6.81) 0.923 1.13 (0.18- 7.18) 0.898 Genus Lachnospiraceae UCG008 111 3.94 (0.89-17.44) 0.0739 1.61 (1.00- 2.58) 0.0477 1.38 (0.36- 5.28) 0.639 1.42 (0.39- 5.09) 0.595 Genus Ruminococcus gauvreauii group 116 0.80 (0.29-2.22) 0.663 0.66 (0.47-0.94) 0.0192 0.47 (0.18-1.25) 0.133 0.45 (0.18-1.15) 0.0982 genus Holdemania 113 0.36 (0.10-1.28) 0.116 0.73 (0.49-1.09) 0.127 1.13 (0.31-4.06) 0.852 1.08 (0.33-3.54) 0.900 PM 2.5 Family Family XIII 89 0.65 (0.19-2.26) 0.501 0.70 (0.51-0.98) 0.0352 0.65 (0.28-1.50) 0.318 0.65 (0.30-1.44) 0.293 PM 2.5-10 Order: NB1n 21 0.12 (0.00-7.84) 0.328 0.36 (0.11-1.15) 0.0839 0.60 (0.06-6.51) 0.678 0.60 (0.07-5.48) 0.655 NO2，nitrogen dioxide; PM,particulate matter; IVW, Inverse-variance weighted; OR, Odds ratio ;CI, Confidence interval Additional Declarations No competing interests reported. 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02:08:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5837896/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5837896/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":74217324,"identity":"4f05ad87-5be2-493d-905c-e6806b533da4","added_by":"auto","created_at":"2025-01-20 06:00:21","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":114597,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e","description":"","filename":"FIGURE1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5837896/v1/c87546f5fe075863321ca349.jpg"},{"id":74217325,"identity":"bfa20403-3a5c-4dae-a4a9-7b4e1d958646","added_by":"auto","created_at":"2025-01-20 06:00:21","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":430749,"visible":true,"origin":"","legend":"\u003cp\u003eLegend not included with this version.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"FIGURE2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5837896/v1/cf240a5de1e29b3895b76860.jpg"},{"id":74217886,"identity":"2b7bc275-2406-4dae-a515-a5d27b7ed37b","added_by":"auto","created_at":"2025-01-20 06:08:29","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1144955,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eScatterplot of two significant associations of air pollutants with six gut microbiotas.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"FIGURE3.png","url":"https://assets-eu.researchsquare.com/files/rs-5837896/v1/4705c9c900cc7ec4b0616e86.png"},{"id":74349730,"identity":"5bbd8612-6652-4e93-a6f5-eea253f435aa","added_by":"auto","created_at":"2025-01-21 10:32:49","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4769656,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5837896/v1/0bee5559-cbe5-43fa-aae5-aa5f68afff03.pdf"},{"id":74217329,"identity":"ea50a39c-b74e-457a-ab8b-9308c7fa96f6","added_by":"auto","created_at":"2025-01-20 06:00:21","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":295962,"visible":true,"origin":"","legend":"","description":"","filename":"TableS1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5837896/v1/094a28c395485983a408b636.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eTwo-sample Mendelian randomization analyses support cross-talk between air pollution exposure and gut microbiome\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAir pollutants typically include a spectrum of different sizes of particulate matter (PM), nitrogen oxides, and numerous other harmful components. In recent years, there has been growing attention to the association between air pollution exposure and human health. Studies have shown that particulate matter air pollution surpassed high systolic blood pressure, smoking, low birth weight and preterm birth, as well as high fasting blood glucose levels in contributing to the global disease burden in 2021[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe mechanisms underlying how air pollution plays negative roles in health are not fully understood, but one of the hypotheses concerns its influence on the gut microbiome[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Disruptions in the gut microbiota can affect human health and are associated with conditions such as irritable bowel syndrome (IBS), liver disease and inflammatory bowel disease[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Research suggests that the microbial makeup in individuals with IBS deviates from that of healthy counterparts, marked by a decrease in beneficial bacteria and a rise in harmful ones. Specifically, changes in the levels of bacteria like the genus \u003cem\u003eRuminococcus gauvreauii\u003c/em\u003e group have been strongly correlated with the onset of IBS. And the severity and duration of IBS symptoms have been associated with disruptions in gut microbiota balance. Meanwhile, the dynamic equilibrium of the gut microbiome can be disrupted by a range of factors, including genetics, aging, lifestyle choices, and environmental conditions[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Research indicates that air pollutants can negatively impact the gastrointestinal system[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] and chronic exposure to PM\u003csub\u003e2.5\u003c/sub\u003e and its components have been linked to gut microbiota dysbiosis in both adults and children[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. There's also emerging evidence suggesting that air pollutants can affect the relative population of gut bacteria[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThus far, there have been few studies conducted on human that explore the relationship between exposure to air contaminants and the diversity and functional capacity of the gut microbiome[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Recently, the MiBioGen consortium unveiled numerous genetic loci associated with variations in microbiome abundance, creating an unparalleled opportunity to probe the potential cause-and-effect relationship connecting the gut microbiota and air pollutants. Mendelian randomization (MR) is a suitable tool to evaluate the genuine causal association between exposure and outcomes by capitalizing on genetic variants. One of the advantages of MR is its ability to overcome confounding and reverse causation, which are common limitations in observational studies. Since genetic variants are determined at conception and remain fixed throughout life, they are not influenced by environmental or behavioral factors that may confound observational associations. This helps establish temporality, ensuring that the exposure precedes the outcome, thus strengthening the inference of causality. Moreover, MR can provide more reliable estimates of causal effects compared to observational studies by mimicking the random allocation of treatments in a controlled trial. Therefore, Mendelian randomization is increasingly being applied. For instance, some scholars have used the Mendelian randomization method to study the relationship between gut diseases and mental disorders, achieving significant progress[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].This allows for a more robust assessment of causal relationships, especially when randomized controlled trials are not feasible or ethical.\u003c/p\u003e \u003cp\u003eTherefore, we proposed a potential correlation between air pollutants, specifically the residential exposures to PM\u003csub\u003e2.5\u003c/sub\u003e, PM\u003csub\u003e10\u003c/sub\u003e, NO\u003csub\u003e2\u003c/sub\u003e and NOx, and alterations in the gut microbiome, using an MR analysis to reveal the underlying causal connection.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003e2.1 Study design\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUtilizing a two-sample MR approach, we evaluated the causal impact of PM\u003csub\u003e2.5\u003c/sub\u003e, PM\u003csub\u003e10\u003c/sub\u003e, PM\u003csub\u003e2.5~10\u003c/sub\u003e, NO\u003csub\u003e2\u003c/sub\u003e, and NO\u003csub\u003ex\u003c/sub\u003e on gut microbiota. To thoroughly explore the influence of air pollutants on gut microbiota, we conducted MR analyses across five specific feature levels, including phylum, class, order, family, and genus. The design of the study, along with the essential MR assumptions, is depicted in Figure 1.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2 Data sources\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data for the exposures, namely PM\u003csub\u003e2.5\u003c/sub\u003e, PM\u003csub\u003e10\u003c/sub\u003e, PM\u003csub\u003e2.5~10\u003c/sub\u003e, NO\u003csub\u003e2\u003c/sub\u003e, and NO\u003csub\u003eX\u003c/sub\u003e, were collected from the summary datasets of GWAS from the UK Biobank, a substantial prospective study encompassing over half a million participants in UK[16]. Comprehensive procedures related to phenotyping, genetic specifics, genome-wide genotyping, imputation, and quality control of UK Biobank participants have been meticulously outlined in a separate report[17]. It should be noted that all participants gave their informed consent in the original studies from which the data were gathered. The GWAS summary dataset for PM\u003csub\u003e2.5\u003c/sub\u003e (identified as GWAS ID: ukb-b10817) encompassed 423,796 participants, all of whom were of European descent. The concentrations of PM\u003csub\u003e2.5\u0026nbsp;\u003c/sub\u003eat the residences of the participants were determined using a land use regression (LUR) model as previously described[18].\u003c/p\u003e\n\u003cp\u003eThe genetic data on gut microbiota were fetched from the most extensive genome-wide association study (GWAS) performed by the MiBioGen consortium, which included 18,340 participants from 24 cohorts[19]. The GWAS encompassed 211 taxa, broken down into 9 phyla, 16 classes, 20 orders, 36 families, and 131 genera. The microbial composition was identified by targeting three specific regions within the 16S rRNA gene. Additional details about the microbiota are available in the referenced study[19], and the GWAS data can be accessed at https://mibiogen.gcc.rug.nl/.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3 Selection of instrumental variables (IVs)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs depicted in Figure 1, for the creation of valid IVs, genetic variations must adhere to three key assumptions of MR as follows: (1) The genetic IVs for air pollutants (such as PM\u003csub\u003e2.5\u003c/sub\u003e, PM\u003csub\u003e10\u003c/sub\u003e, PM\u003csub\u003e2.5~10\u003c/sub\u003e, NO\u003csub\u003e2\u003c/sub\u003e, and NO\u003csub\u003ex\u003c/sub\u003e) must exhibit a significant correlation with levels of exposure to these pollutants. (2) The association between these genetic IVs and gut microbiota should be completely independent of any confounding factors. (3) The genetic IVs for environmental pollutants can only influence gut microbiota through exposure to environmental pollutants. Our research abides by the guidelines set forth in the Strengthening the Reporting of Observational Studies in Epidemiology using Mendelian Randomization (STROBE-MR) as established in a previous study[20].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe classified 211 bacterial taxa into five taxonomic ranks: phylum, class, order, family, and genus. However, 15 of these taxa remained unidentified, and we consequently excluded them from our investigation, leaving us with 196 bacterial taxa. We determined instrumental variables (IV) through a systematic approach. First, we identified all SNPs significantly associated with air pollution, applying a stringent threshold of genome-wide significance (P \u0026lt; 5\u0026times;10\u003csup\u003e-8\u003c/sup\u003e) to select our IVs. However, when extracting SNPs that robustly predicted the gut microbiome at this significance level, we found that only a limited number were available, resulting in insufficient SNPs for our analysis. Consequently, we opted for a more inclusive threshold (P \u0026lt; 1\u0026times;10\u003csup\u003e-5\u003c/sup\u003e) to gather the necessary SNPs for the two-sample Mendelian randomization analysis. [21, 22] .\u003c/p\u003e\n\u003cp\u003eIn line with prior research, we grouped the genetic variants located within 250 kb at a linkage disequilibrium (LD) threshold of r\u003csup\u003e2\u003c/sup\u003e\u0026lt;0.1. This was based on reference data of European ancestry from the 1,000 Genomes Project[23]. For each SNP, we calculated the F-statistic to measure the statistical power, discarding any that had an F-statistic below 10. The process for calculating the F-statistic has been elaborated on in other sources[22]. Lastly, through harmonization, we ensured the effect alleles of the exposure and outcome SNPs were aligned. We eliminated any SNPs that had inconsistent alleles (for example, A/C combined with A/G), or those that were palindromic with a moderate allele frequency.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4 Statistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor univariable Mendelian Randomization (MR) analysis, we employed the inverse-variance weighted (IVW) method with random effects as the primary tool to infer causality. This involved combining Wald ratio estimates to yield a pooled effect on the outcome[24]. We considered the potential heterogeneity between Wald ratio estimates from SNPs[25]. Therefore, in the IVW method, heterogeneity was assessed by Cochran\u0026apos;s Q\u0026apos; (modified) test. The odds ratio (OR) and 95% confidence interval (CI) represent the results. To test the resilience of the MR results, we utilized several alternative models, including the weighted median, MR-Egger, simple mode, and weighted mode methods[26, 27]. The weighted median method assumes that less than 50% of the SNPs are invalid. Its statistical power is slightly weaker than that of the IVW method[28]. The MR-Egger method, on the other hand, has relatively low power and is primarily employed for determining directionality[29]. To determine the statistical significance of the MR effect estimate, a false discovery rate (FDR) of less than 5% was used. For this purpose, we employed the Benjamini-Hochberg procedure, which is commonly used to correct multiple comparisons. This method adjusts the P-values to control the expected proportion of \u0026quot;discoveries\u0026quot; (rejected null hypotheses) that are false (incorrect rejections). By using this approach, we could account for the number of taxa tested and effectively manage the risk of false positives due to multiple tests. A two-sided P\u003csub\u003eFDR\u003c/sub\u003e value of \u0026lt; 0.05 was statistically significant, while P values less than 0.05 and P\u003csub\u003eFDR\u003c/sub\u003e values more than 0.05 were interpreted as indicating a suggested causal relationship. To scrutinize both substantial and minor causal relationships, we further implemented a set of sensitivity analyses. These analyses allowed us to discern any potential breaches of the MR principles. The \u0026apos;leave one out\u0026apos; (LOO) approach was employed to assess whether a single outlier unduly influenced the overall calculation[30]. And we applied the Cochran Q test to examine the presence of heterogeneity[31]. Furthermore, to identify instances of horizontal pleiotropy, we utilized the intercept term acquired from the MR-Egger regression[26].Through this intercept, researchers can assess whether certain SNPs influence the outcome through pathways other than the exposure, thereby accounting for confounding effects and detecting the presence of pleiotropy. The entirety of these analyses was executed in the R programming environment (version 4.3.0), utilizing the \u0026quot;TwoSampleMR\u0026quot; package (version 0.5.7).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eIn order to comprehend the effects of various air pollutants on the gut microbiota, we conducted separate analyses for distinct air pollutants (NO\u003csub\u003e2\u003c/sub\u003e, NO\u003csub\u003eX\u003c/sub\u003e, PM\u003csub\u003e2.5\u003c/sub\u003e, PM\u003csub\u003e10\u003c/sub\u003e, PM\u003csub\u003e2.5-10\u003c/sub\u003e). In the comprehensive MR analysis, we assessed the associations between air pollutants and a total of 196 bacterial taxa encompassing 9 phyla, 16 classes, 20 orders, 32 families, and 119 genera (Table S1). After meticulous instrument selection processes, the count of SNPs linked with each bacterial taxon varied from a minimum of 3 to a maximum of 117. We assessed the potential bias of weak instrumental variables, and with every F-statistic surpassing 10, the results confirm that no weak IVs were included in our study.\u003c/p\u003e\n\u003cp\u003eFigure 2 displays the initial correlations between five air pollutants (NO2, NOX, PM2.5, PM10, PM2.5-10) and bacterial taxa across various taxonomic levels, derived through the application of the Inverse Variance Weighted (IVW) method.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe Effect of NO\u003csub\u003e2\u003c/sub\u003e on Intestinal Microbiota\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe detected five meaningful associations linked to NO\u003csub\u003e2\u003c/sub\u003e (Table 1). The genetic predisposition to NO\u003csub\u003e2\u003c/sub\u003e exposure significantly correlated with an elevated abundance of the \u003cem\u003eEubacterium fissicatena\u003c/em\u003e \u003cem\u003egroup\u003c/em\u003e genus (IVW-OR=2.20; 95%CI: 1.42\u0026ndash;3.41; \u003cem\u003eP\u003c/em\u003e=4.36*10\u003csup\u003e\u0026minus;4\u003c/sup\u003e;\u003cem\u003e\u0026nbsp;P\u003c/em\u003e\u003csub\u003eFDR\u003c/sub\u003e=0.0227). To perform heterogeneity testing on NO\u003csub\u003e2\u003c/sub\u003e and the \u003cem\u003egenus Eubacterium fissicatena group\u003c/em\u003e, the IVW method yielded Q\u003csub\u003epval\u003c/sub\u003e=0.846, and the MR Egger method yielded Q\u003csub\u003epval\u003c/sub\u003e=0.836. The P-value for the pleiotropy test was 0.525, all of which exceeded 0.05. The \u003cem\u003eGordonibacter\u0026nbsp;\u003c/em\u003egenus (IVW-OR\u003cem\u003e=\u003c/em\u003e2.29; 95%CI: 1.48-3.56, \u003cem\u003eP\u003c/em\u003e=2.17\u0026acute;10\u003csup\u003e-4\u003c/sup\u003e, \u003cem\u003eP\u003c/em\u003e\u003csub\u003eFDR\u003c/sub\u003e=0.0227) also presented an increase in microbial abundance. Using the IVW method (Q\u003csub\u003epval\u003c/sub\u003e=0.804) and the MR Egger method (Q\u003csub\u003epval\u003c/sub\u003e=0.790) did not observe significant heterogeneity. The P-value for pleiotropy test was 0.620.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFurthermore, the abundance of the\u003cem\u003e\u0026nbsp;LachnosPiraceae\u003c/em\u003e genus (IVW-OR=1.82; 95%CI: 1.32-2.51; \u003cem\u003eP\u003c/em\u003e=2.37*10\u003csup\u003e-4\u003c/sup\u003e, \u003cem\u003eP\u003c/em\u003e\u003csub\u003eFDR\u003c/sub\u003e=0.0227) also increased. No significant heterogeneity was found with the IVW method yielding Q\u003csub\u003epval\u003c/sub\u003e=0.490 and the MR Egger method Q\u003csub\u003epval\u003c/sub\u003e=0.492, moreover a pleiotropy test P-value of 0.302.\u003c/p\u003e\n\u003cp\u003eConversely, a lower abundance of the \u003cem\u003eHoldemania\u003c/em\u003e\u003csub\u003e\u0026nbsp;\u003c/sub\u003egenus (IVW-OR=0.62; 95%CI: 0.47-0.81; \u003cem\u003eP\u003c/em\u003e=6.58*10\u003csup\u003e-4\u003c/sup\u003e, \u003cem\u003eP\u003c/em\u003e\u003csub\u003eFDR\u003c/sub\u003e=0.0258). Heterogeneity testing was performed on the results, with the IVW method yielding Q\u003csub\u003epval\u003c/sub\u003e=0.546 and the MR Egger method Q\u003csub\u003epval\u003c/sub\u003e=0.539. The pleiotropy test of the results yielded a P-value of 0.393. Additionally, the \u003cem\u003eRuminococcus gauvreauii\u003c/em\u003e \u003cem\u003egenus\u003c/em\u003e (IVW-OR=0.66; 95%CI: 0.53-0.83; P=4.63*10\u003csup\u003e-4\u003c/sup\u003e, \u003cem\u003eP\u003c/em\u003e\u003csub\u003eFDR\u003c/sub\u003e=0.0227) was observed decreases in relation to genetically predicted NO\u003csub\u003e2\u003c/sub\u003e exposure. Conducting heterogeneity analysis, we obtained Q\u003csub\u003epval\u003c/sub\u003e=721 with the IVW method and 0.702 with the MR Egger method. Furthermore, when assessing the pleiotropy test, we found a P-value of 0.722. The consistent direction and magnitude of estimates from other MR models like Weighted median, Simple mode, Weighted mode, and MR-Egger regression bolstered the causal interpretations (Table 2). The Cochran Q test did not reveal any heterogeneity (Table S1). Furthermore, the MR-Egger intercept analysis indicated an absence of potential horizontal pleiotropy (Table S1). The LOO analysis affirmed that no single SNP was driving these causalities.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe Effect of PM\u003csub\u003e2.5\u003c/sub\u003e on Intestinal Microbiota\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur observations indicated that a genetic predisposition to PM\u003csub\u003e2.5\u003c/sub\u003e exposure correlated with a decreased presence of \u003cem\u003eFamily XIII\u003c/em\u003e (IVW-OR=0.69 95%CI 0.55-0.87, \u003cem\u003eP\u003c/em\u003e=1.47*10\u003csup\u003e-3\u003c/sup\u003e,\u003cem\u003e\u0026nbsp;P\u003c/em\u003e\u003csub\u003eFDR\u003c/sub\u003e=0.0471) (Table 1). Regarding this result, heterogeneity testing was conducted using the IVW method (Q\u003csub\u003epval\u003c/sub\u003e=0.432) and the MR Egger method (Q\u003csub\u003epval\u003c/sub\u003e=0.403). The pleiotropy test result yielded a P-value of 0.924. The causal associations were further substantiated by the consistent direction and magnitude of estimates from various other MR models. The study revealed no signs of heterogeneity or potential horizontal pleiotropy. Furthermore, the LOO analysis verified that these causalities were not influenced by any individual SNP. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe other pollutants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA lower abundance of the \u003cem\u003eorder NB1n\u003c/em\u003e was observed (IVW-OR=0.25; 95%CI: 0.11-0.59; \u003cem\u003eP\u003c/em\u003e=1.52*10\u003csup\u003e-3\u003c/sup\u003e, \u003cem\u003eP\u003c/em\u003e\u003csub\u003eFDR\u003c/sub\u003e=0.0304). Heterogeneity testing was conducted, with the IVW method yielding Q\u003csub\u003epval\u003c/sub\u003e=0.766 and the MR Egger method Q\u003csub\u003epval\u003c/sub\u003e=0.720. The pleiotropy test of the results yielded a P-value of 0.712.\u003c/p\u003e\n\u003cp\u003eNo associations were found between genetically predicted exposures to PM\u003csub\u003e10\u003c/sub\u003e, PM\u003csub\u003e2.5-10\u003c/sub\u003e, and NO\u003csub\u003ex\u003c/sub\u003e and the abundance of gut microbiota (Figure 2).\u003c/p\u003e\n\u003cp\u003eIt is noteworthy that some results showed \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05(Table 1), but \u003cem\u003eP\u003c/em\u003e\u003csub\u003eFDR\u003c/sub\u003e \u0026gt; 0.05, indicating a potential association between the two that warrants further invest. The heterogeneity test for the genus Holdemania showed Q\u003csub\u003eMR-Egger\u003c/sub\u003e p = 0.015 and Q \u003csub\u003eIVW\u003c/sub\u003e p = 0.005, with pleiotropy p = 0.021. These results suggest potential pleiotropy, which contradicts the Mendelian assumption. As a result, the findings may not hold significant relevance, and further investigation is needed to explore how other factors may influence the gut microbiota associated with this genus.\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eIn the course of our study, we have identified 5 gut microbiota genus that are impacted by air pollution. Specifically, 4 of them are influenced by NO\u003csub\u003e2\u003c/sub\u003e, including the \u003cem\u003eEubacterium fissicatena group\u003c/em\u003e, \u003cem\u003eGordonibacter\u003c/em\u003e, \u003cem\u003eLachnosPiraceae\u003c/em\u003e, \u003cem\u003eHoldemania\u003c/em\u003e and \u003cem\u003eRuminococcus gauvreauii\u003c/em\u003e genus, and \u003cem\u003eFamily XIII\u003c/em\u003e is affected by PM2.5.\u003c/p\u003e \u003cp\u003eGiven that established factors like diet and antibiotic usage account for only 16% of the variability in gut microbiome composition among individuals[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], recent research has pivoted towards investigating the relationship between air pollution and the gut microbiome. There are divergent findings in studies examining the associations between air pollution exposure and indices of gut microbiome diversity, evenness, and richness. While the majority of the reviewed publications have indicated alterations in microbial phyla following exposure, it is crucial to acknowledge the significant disparities observed across various studies[\u003cspan additionalcitationids=\"CR35 CR36\" citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Apart from \u003cem\u003eVerrucromicrobia\u003c/em\u003e, no other taxonomic groups exhibited consistent findings across multiple animal studies, potentially indicative of confounding variables inherent to animal experimentation[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Consequently, these studies can only be interpreted as evidence of a correlation between exposure to air pollution and a modified composition of the animal gut microbiome. Notably, it should be recognized that animal studies on air pollution involve the administration of high doses of toxic compounds within a relatively short timeframe, which diverges from the chronic, low-level exposure characteristic of human scenarios. The consistent constraint observed in the reviewed studies lies in the absence of comprehensive compositional analyses concerning the particulate matter and gases to which both mice and humans were subjected.\u003c/p\u003e \u003cp\u003eMeanwhile, MR provides a novel approach to investigate the relationship between air pollution and gut microbiota. It can overcome the confounding and reverse causation typical in observational studies, thus providing more reliable estimates of causal effects, and also avoid biases inherent in animal studies, where short-term exposure to high doses of pollutants differs from the chronic, low-level exposure typical in human scenarios. Therefore, we decided to delve into the potential causal link between environmental contaminants (specifically PM\u003csub\u003e2.5\u003c/sub\u003e, PM\u003csub\u003e10\u003c/sub\u003e, PM\u003csub\u003e2.5-10\u003c/sub\u003e, NO\u003csub\u003e2\u003c/sub\u003e, and NO\u003csub\u003ex\u003c/sub\u003e) and human gut microbiota leveraging the MR design. However, MR has limitations and potential biases. It assumes genetic variants are only associated with the outcome through the exposure of interest, but violations like pleiotropy can bias results. Additionally, MR needs large sample sizes and relies on assumptions about genetic instruments, measurement error, and population stratification, which can also introduce bias if not properly addressed.\u003c/p\u003e \u003cp\u003eA decline in the abundance of the \u003cem\u003eRuminococcus gauvreauii\u003c/em\u003e group can impact acetate synthesis, leading to a reduction in acetate levels. This group of bacteria can generate acetate from pyruvate via the acetyl-CoA pathway[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Acetate represents one of the most prevalent SCFA in the peripheral circulation. Additionally, acetate has the ability to permeate the blood-brain barrier, influencing appetite reduction via a central regulatory mechanism[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Moreover, acetate has been found to attenuate allergic airway disease by boosting regulatory T cells (Tregs)[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAn escalated abundance of the \u003cem\u003eEubacterium fissicatena\u003c/em\u003e group genus can induce a surge in butyrate production[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Butyrate not only significantly influences colonocyte proliferation and apoptosis but also modulates immune responses. An increase in butyrate production within the intestine can lead to the onset of irritable bowel syndrome[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. An increased presence of the \u003cem\u003eEubacterium fissicatena\u003c/em\u003e group genus can enhance the transformation of acetate to butyrate, thereby leading to a rise in butyrate and a further decrease in acetate[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe \u003cem\u003eGordonibacter\u003c/em\u003e genus is part of the \u003cem\u003eActinobacteria Phylum\u003c/em\u003e, characterized by Gram-positive bacteria with a high guanine and cytosine content. Notably, the \u003cem\u003eGordonibacter\u003c/em\u003e genus is closely associated with the metabolism of polyphenols[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. \u003cem\u003eHoldemania\u003c/em\u003e, which ranks among the top 30 most abundant genera in the gut microbiome of individuals from affluent countries[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e], is also frequently observed in the Midwestern Reference Panel (MWRP), a resource used for comparative gut microbiome studies[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. On the other hand, the \u003cem\u003eLachnosPiraceae\u003c/em\u003e, another significant group within the \u003cem\u003eFirmicutes\u003c/em\u003e phylum, appears to react to the metabolic health status of the host. Elevated abundances of this group have been observed in patient groups exhibiting impaired glucose metabolism[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. \u003cem\u003eLachnosPiraceae\u003c/em\u003e has been recognized as SCFA producers that positively influence the intestinal barrier[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFor the first time, we employed the MR methodologies to conduct a large-scale causal analysis linking long-term exposure to air pollution levels in the same population with the abundance of gut microbiota. This approach mitigated confounding factors and addressed the limitations associated with short exposure observation periods in animal experiments. In future research endeavors, it is recommended to measure the elemental, organic, biological/microbiological, ionic, and carbonaceous components of particulate matter. Additionally, the gaseous phase should be examined for common pollutants, along with the analysis of volatile organic compounds. These measurements will aid in establishing causal relationships between atmospheric components and the observed alterations in the gut microbiome. Furthermore, assessments should be conducted to evaluate how pollutants and inhaled microbes may transform within the lungs and esophagus during inhalation and subsequent ingestion.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, our study provides evidence linking air pollutants, especially NO2 and PM2.5, to significant changes in the composition of the gut microbiota, impacting genera such as \u003cem\u003eEubacterium fissicatena group\u003c/em\u003e, \u003cem\u003eGordonibacter\u003c/em\u003e, \u003cem\u003eLachnosPiraceae\u003c/em\u003e, \u003cem\u003eHoldemania\u003c/em\u003e, and \u003cem\u003eRuminococcus gauvreauii\u003c/em\u003e. These findings underscore the potential health implications of air pollution on overall well-being through its influence on the gut microbiota. Moving forward, further research should focus on elucidating the mechanistic pathways driving these associations and exploring therapeutic strategies aimed at modulating the gut microbiota to mitigate the adverse effects of air pollution on human health. Additionally, investigating interventions like dietary adjustments, probiotics, or targeted therapies could help alleviate the detrimental impacts of air pollutants on the gut microbiome and overall health. These insights underscore the necessity for interdisciplinary collaborations integrating environmental science, microbiology, and clinical medicine to develop effective strategies for reducing the health risks associated with air pollution.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData accessibility statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe original data sources used to support our findings of this study are accessed from the public summary GWAS resources. All detailed research approaches for the two-sample Mendelian randomization are retrieved online from every GWAS consortia.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of Competing Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no potential conflicts of interest.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors are grateful to the participants and investigators of the included public GWAS studies.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was funded by the National Natural Science Foundation of China (82103912), the Taishan Scholars Program of Shandong Province (tsqn202312328), and the Excellent Youth Innovation Team of Shandong Provincial Higher Education Institutions (2022KJ012). The funding bodies played no part in the study\u0026apos;s conception, data collection, data analysis, data interpretation, or manuscript preparation. All authors have unrestricted access to the study data, with the final decision to submit the manuscript for publication resting with the corresponding author.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStudy design: Xiaorong Yang and Xiaolin Yin;\u003c/p\u003e\n\u003cp\u003eData collection: Shaowei Gu;\u003c/p\u003e\n\u003cp\u003eData analyses: Shaowei Gu, Yikun Cui, Hui Chen, Hao Bai;\u003c/p\u003e\n\u003cp\u003eResults visualization: Shaowei Gu;\u003c/p\u003e\n\u003cp\u003eResults interpretations: All authors;\u003c/p\u003e\n\u003cp\u003eManuscript writing: Shaowei Gu;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eManuscript revising: All authors;\u003c/p\u003e\n\u003cp\u003eAll authors approved the reported manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed Consent Statement:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInformed consent of participants was 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their FODMAP content alter the colonic luminal microenvironment\u003c/strong\u003e. \u003cem\u003eGut \u003c/em\u003e2015, \u003cstrong\u003e64\u003c/strong\u003e(1):93-100.\u003c/li\u003e\n\u003cli\u003eTomas-Barberan FA, Selma MV, Espin JC: \u003cstrong\u003eInteractions of gut microbiota with dietary polyphenols and consequences to human health\u003c/strong\u003e. \u003cem\u003eCurr Opin Clin Nutr Metab Care \u003c/em\u003e2016, \u003cstrong\u003e19\u003c/strong\u003e(6):471-476.\u003c/li\u003e\n\u003cli\u003eArumugam M, Raes J, Pelletier E, Le Paslier D, Yamada T, Mende DR, Fernandes GR, Tap J, Bruls T, Batto JM\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eEnterotypes of the human gut microbiome\u003c/strong\u003e. \u003cem\u003eNature \u003c/em\u003e2011, \u003cstrong\u003e473\u003c/strong\u003e(7346):174-180.\u003c/li\u003e\n\u003cli\u003eChen J, Ryu E, Hathcock M, Ballman K, Chia N, Olson JE, Nelson H: \u003cstrong\u003eImpact of demographics on human gut microbial diversity in a US Midwest population\u003c/strong\u003e. \u003cem\u003ePeerJ \u003c/em\u003e2016, \u003cstrong\u003e4\u003c/strong\u003e:e1514.\u003c/li\u003e\n\u003cli\u003eLippert K, Kedenko L, Antonielli L, Kedenko I, Gemeier C, Leitner M, Kautzky-Willer A, Paulweber B, Hackl E: \u003cstrong\u003eGut microbiota dysbiosis associated with glucose metabolism disorders and the metabolic syndrome in older adults\u003c/strong\u003e. \u003cem\u003eBenef Microbes \u003c/em\u003e2017, \u003cstrong\u003e8\u003c/strong\u003e(4):545-556.\u003c/li\u003e\n\u003cli\u003eSchnorr SL, Candela M, Rampelli S, Centanni M, Consolandi C, Basaglia G, Turroni S, Biagi E, Peano C, Severgnini M\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eGut microbiome of the Hadza hunter-gatherers\u003c/strong\u003e. \u003cem\u003eNat Commun \u003c/em\u003e2014, \u003cstrong\u003e5\u003c/strong\u003e:3654.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1. Significant MR results of causal links between air pollutants and gut microbiome by using the IVW method.\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"907\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eExposure\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 312px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOutcome\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo. SNP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR (95%CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP\u003csub\u003eIVW\u003c/sub\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP\u003csub\u003eFDR\u003c/sub\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP\u003csub\u003epleiotropy\u003c/sub\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP\u003csub\u003eHeterogeneity\u003c/sub\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"20\" style=\"width: 76px;\"\u003e\n \u003cp\u003eNO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eGenus: Gordonibacter\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e2.29 (1.48- 3.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e2.17\u0026acute;10\u003csup\u003e-4\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.0227\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.620\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0.804\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eGenus: Lachnospiraceae UCG008\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e1.82 (1.32- 2.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e2.37\u0026acute;10\u003csup\u003e-4\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.0227\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.302\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0.490\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eGenus: Eubacterium fissicatena group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e2.20 (1.42- 3.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e4.36\u0026acute;10\u003csup\u003e-4\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.0227\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.525\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0.846\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eGenus: Ruminococcus Gauvreauii group\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e116\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e0.66 (0.53-0.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e4.62\u0026acute;10\u003csup\u003e-4\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.0227\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.722\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0.722\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eGenus: Holdemania\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e113\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e0.62 (0.47-0.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e6.58\u0026acute;10\u003csup\u003e-4\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.0258\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.393\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0.546\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eFamily: Peptococcaceae\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e114\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e1.54 (1.18-2.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e1.75\u0026acute;10\u003csup\u003e-3\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.0571\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.590\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0.202\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eGenus: Defluviitaleaceae UCG011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e112\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e0.64 (0.47-0.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e2.63\u0026acute;10\u003csup\u003e-3\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.0736\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.641\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0.659\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eFamily: Defluviitaleaceae\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e112\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e0.65 (0.49-0.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e4.51\u0026acute;10\u003csup\u003e-3\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.594\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0.587\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eGenus: Barnesiella\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e116\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e0.73 (0.58-0.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e4.98\u0026acute;10\u003csup\u003e-3\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.929\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0.424116\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eGenus: Bilophila\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e116\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e0.73 (0.58-0.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e8.34\u0026acute;10\u003csup\u003e-3\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.164\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.396\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0.922\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eFamily: Bacteroidaceae\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e117\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e0.78 (0.64-0.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.0130\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.205\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.907\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0.364\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eGenus: Bacteroides\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e117\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e0.78 (0.64-0.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.0130\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.205\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.907\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0.364\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eGenus: Lactococcus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e1.79 (1.13- 2.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.0136\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.205\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.766\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0.103\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eGenus: Lachnospiraceae UCG010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e116\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e0.73 (0.57-0.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.0152\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.213\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.0808\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0.631\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eGenus: Eubacterium ventriosum group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e116\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e0.78 (0.63-0.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.0231\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.301\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.856\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0.981\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eFamily: Porphyromonadaceae\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e117\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e0.80 (0.65-0.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.0273\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.332\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0.235\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eFamily: Veillonellaceae\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e117\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e1.26 (1.02-1.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.0288\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.332\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.775\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0.591\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eGenus: Ruminiclostridium6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e116\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e0.77 (0.60-0.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.042\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.425\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.693\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0.0663\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eFamily: Clostridiales vadin BB60 group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e1.31 (1.01-1.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.0403\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.438\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.800\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0.734\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eGenus: Eubacterium coprostanoligenes Group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e117\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e1.22 (1.00-1.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.0449\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.0449\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0.952\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"7\" style=\"width: 76px;\"\u003e\n \u003cp\u003ePM\u003csub\u003e2.5\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eFamily: Family: XIII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e0.69 (0.55-0.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e1.47\u0026acute;10-3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.0471\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.924\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0.432\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eGenus: Defluviitaleaceae UCG011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e0.66 (0.48-0.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.0104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.783\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.904\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0.783\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eFamily: Defluviitaleaceae\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e0.68 (0.49-0.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.0171\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.203\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.854\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0.752\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eFamily: Peptostreptococcaceae\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e1.31 (1.05-1.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.0190\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.203\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.0637\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0.678\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eGenus: Tyzzerella3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e0.63 (0.42- 0.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.0256\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.783\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.491\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0.689\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eGenus: Holdemania\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e0.66 (0.46-0.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.0261\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.783\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.0205\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0.00539\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eGenus: Lachnospiraceae NC2004 group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e0.63 (0.42-0.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.0263\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.783\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.238\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0.118\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"10\" style=\"width: 76px;\"\u003e\n \u003cp\u003ePM\u003csub\u003e10\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eGenus: Ruminococcaceae UCG009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e0.45 (0.26- 0.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e4.96\u0026acute;10-3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.567\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.0541\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0.444\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eGenus: Eubacterium xylanophilum group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e0.60 (0.39-0.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.0164\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.567\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.501\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0.418\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eGenus: Faecalibacterium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e0.65 (0.46-0.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.0189\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.567\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.814\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0.828\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eGenus: Eubacterium ventriosum group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e0.62 (0.42-0.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.0192\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.567\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.572\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0.413\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eGenus: Alloprevotella\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e0.25 (0.08-0.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.0246\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.581\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.912\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0.442\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eGenus: Parabacteroides\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e1.49 (1.04-2.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.592\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.864\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0.578\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003ePhylum: Bacteroidetes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e1.47 (1.02- 2.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.0413\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.207\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.375\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0.321\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eClass: Bacteroidia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e1.46 (1.01- 2.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.0450\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.407\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.416\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0.277\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eOrder: Bacteroidales\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e1.46 (1.01- 2.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.0450\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.625\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.416\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0.277\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eFamily: Ruminococcaceae\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e0.68 (0.47-0.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.0447\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.660\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.277\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0.287\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\" style=\"width: 76px;\"\u003e\n \u003cp\u003ePM\u003csub\u003e2.5-10\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eOrder: NB1n\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e0.25 (0.11-0.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e1.52\u0026acute;10\u003csup\u003e-3\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.0304\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.712\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0.766\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eGenus: Family XIII UCG001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e1.91 (1.11- 3.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.0204\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.826\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.816\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0.364\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eGenus: Lachnospiraceae UCG010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e1.80 (1.08- 3.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.0236\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.826\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.283\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0.891\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eGenus: Ruminococcus gauvreauii group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e0.56 (0.34-0.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.0238\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.826\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.707\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0.781\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eGenus: Allisonella\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e3.70 (1.12-2.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.0319\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.826\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.178\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0.346\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"7\" style=\"width: 76px;\"\u003e\n \u003cp\u003eNO\u003csub\u003eX\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eGenus: Lachnospiraceae UCG001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e112\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e1.42 (1.05- 1.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.0212\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.846\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.0108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0.0190\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eGenus: Ruminococcaceae UCG013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e114\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e0.79 (0.64-0.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.0247\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.846\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.417\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0.735\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eGenus: Lachnospiraceae UCG008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e112\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e1.43 (1.04- 1.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.0287\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.846\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.595\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0.768\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eGenus: Ruminococcaceae UCG005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e114\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e1.26 (1.01-1.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.0374\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.846\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.805\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0.273\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eGenus: Hungatella\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e1.53 (1.01- 2.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.0464\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.846\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.318\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0.547\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eGenus: Gordonibacter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e1.55 (1.00-2.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.0497\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.846\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.636\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0.871\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eGenus: Holdemania\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e112\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e0.69 (0.52-0.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.0973\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.846\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.0187\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0.835\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eNO\u003csub\u003e2\u003c/sub\u003e，nitrogen dioxide; PM,particulate matter; IVW, Inverse-variance weighted; \u0026nbsp;OR, Odds ratio ;CI, Confidence interval\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;The Results of the other four Mendelian randomization methods.\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"784\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003eSNPs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eMR.Egger\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eWeighted.median\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eSimple mode\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eWeighted mode\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003eAir pollutants\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003eGut microbiota\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e(n)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eOR(95％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eOR(95％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eOR(95％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eOR(95％)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\" style=\"width: 56px;\"\u003e\n \u003cp\u003eNO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003eGenus Eubacterium fissicatena group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e1.13 (0.14- 9.09)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.907\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e2.42 (1.31- 4.46)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.00475\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e3.34 (0.48-23.02)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.223\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e3.25 (0.55-19.32)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.198\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003eGenus Gordonibacter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e3.85 (0.48-31.15)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.209\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e2.00 (1.08- 3.72)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.0279\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e1.09 (0.18- 6.81)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.923\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e1.13 (0.18- 7.18)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.898\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003eGenus Lachnospiraceae UCG008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e3.94 (0.89-17.44)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.0739\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e1.61 (1.00- 2.58)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.0477\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e1.38 (0.36- 5.28)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.639\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e1.42 (0.39- 5.09)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.595\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003eGenus Ruminococcus gauvreauii group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e116\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.80 (0.29-2.22)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.663\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.66 (0.47-0.94)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.0192\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.47 (0.18-1.25)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.133\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.45 (0.18-1.15)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.0982\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003egenus Holdemania\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e113\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.36 (0.10-1.28)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.116\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.73 (0.49-1.09)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.127\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e1.13 (0.31-4.06)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.852\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e1.08 (0.33-3.54)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.900\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003ePM\u003csub\u003e2.5\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003eFamily Family XIII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.65 (0.19-2.26)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.501\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.70 (0.51-0.98)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.0352\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.65 (0.28-1.50)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.318\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.65 (0.30-1.44)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.293\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003ePM\u003csub\u003e2.5-10\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003eOrder: NB1n\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.12 (0.00-7.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.328\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.36 (0.11-1.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.0839\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.60 (0.06-6.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.678\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.60 (0.07-5.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.655\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eNO2，nitrogen dioxide; PM,particulate matter; IVW, Inverse-variance weighted; \u0026nbsp;OR, Odds ratio ;CI, Confidence interval\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Air pollution, Gut microbiome, Mendelian randomization, Particulate matter, Nitrogen oxide","lastPublishedDoi":"10.21203/rs.3.rs-5837896/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5837896/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eVarious studies have suggested the intriguing potential of air pollution exposure to influence gut microbiota diversity. It can impact gut microbiota not only by directly entering the intestine, but also through the gut-lung axis when deposited in lungs. Nevertheless, the scarcity of compelling genetic causal evidence remains conspicuous. Our objective was to evaluate whether a genetic causal relationship exists between air pollution and gut microbiota, along with the potential implications of this connection.\u003c/p\u003e\u003ch2\u003eMethod\u003c/h2\u003e \u003cp\u003eThis study was designed to investigate the link between air pollutant exposure (encompassing PM\u003csub\u003e2.5\u003c/sub\u003e, PM\u003csub\u003e10\u003c/sub\u003e, PM\u003csub\u003e2.5\u0026minus;10\u003c/sub\u003e, NO\u003csub\u003e2\u003c/sub\u003e, and NO\u003csub\u003ex\u003c/sub\u003e) and alterations in the gut microbiome using a two-sample Mendelian randomization method based on summary-level GWAS study. To explore the effect of air pollutants on gut microbiota, we conducted MR analyses across five specific feature levels, including phylum, class, order, family, and genus. The main analytical approach employed was inverse variance weighting (IVW), which examined the relationship between exposure and outcome by assessing single nucleotide polymorphisms (SNPs) linked to air pollution.. Additional sensitivity analyses, such as Cochran Q test, MR-Egger regression, and leave-one-out analysis, were conducted to evaluate the robustness of the findings.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA statistically noteworthy association was observed between NO\u003csub\u003e2\u003c/sub\u003e exposure and an uptick in the genus \u003cem\u003eEubacterium fissicatena\u003c/em\u003e group [IVW-odds ratio (\u003cem\u003eOR\u003c/em\u003e)\u0026thinsp;=\u0026thinsp;2.20; 95% confidence interval (\u003cem\u003eCI\u003c/em\u003e), 1.42\u0026ndash;3.41; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4.36*10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e], the \u003cem\u003eGordonibacter\u003c/em\u003e genus (IVW-\u003cem\u003eOR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.29; 95%CI: 1.48\u0026ndash;3.56; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.17*10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e), and the \u003cem\u003eLachnosPiraceae\u003c/em\u003e genus (IVW-OR\u0026thinsp;=\u0026thinsp;1.82; 95%CI: 1.32\u0026ndash;2.51; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.37*10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e). Contrarily, a decrease in the abundance of the \u003cem\u003eHoldemania\u003c/em\u003e genus (IVW-OR\u0026thinsp;=\u0026thinsp;0.616; 95%CI: 0.47\u0026ndash;0.81; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;6.58*10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e) and the \u003cem\u003eRuminococcus gauvreauii\u003c/em\u003e genus (IVW-OR\u0026thinsp;=\u0026thinsp;0.663; 95%CI: 0.53\u0026ndash;0.83; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4.63*10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e) was linked with NO\u003csub\u003e2\u003c/sub\u003e exposure. Furthermore, PM\u003csub\u003e2.5\u003c/sub\u003e exposure was associated with a lower presence of \u003cem\u003eFamily XIII\u003c/em\u003e (IVW-OR\u0026thinsp;=\u0026thinsp;0.691; 95%CI: 0.55\u0026ndash;0.87; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.47*10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eOur findings indicate air pollutants, particularly NO\u003csub\u003e2\u003c/sub\u003e and PM\u003csub\u003e2.5\u003c/sub\u003e, appeared to have a noteworthy association with the gut microbiota's composition, especially for genus \u003cem\u003eEubacterium fissicatena\u003c/em\u003e group, \u003cem\u003eGordonibacter\u003c/em\u003e genus, \u003cem\u003eLachnosPiraceae\u003c/em\u003e genus, \u003cem\u003eHoldemania\u003c/em\u003e genus and the \u003cem\u003eRuminococcus gauvreauii\u003c/em\u003e genus. This may offer valuable insights for further investigations into the mechanisms and clinical implications of air pollution-induced dysbiosis of the gut microbiome.\u003c/p\u003e","manuscriptTitle":"Two-sample Mendelian randomization analyses support cross-talk between air pollution exposure and gut microbiome","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-20 05:44:16","doi":"10.21203/rs.3.rs-5837896/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"70d2a77e-af82-4e46-899c-f636fb9d1d45","owner":[],"postedDate":"January 20th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":43099640,"name":"Earth and environmental sciences/Environmental sciences/Environmental impact"},{"id":43099641,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2025-01-21T10:24:19+00:00","versionOfRecord":[],"versionCreatedAt":"2025-01-20 05:44:16","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5837896","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5837896","identity":"rs-5837896","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}