Gut microbiota, circulating inflammatory proteins, and  cirrhosis: a multivariable Mendelian randomization study

doi:10.21203/rs.3.rs-5318866/v1

Background

The liver-gut axis is the focal point of cirrhosis research, suggesting a close association between the gut microbiota (GM) and cirrhosis. Previous studies have shown a significant correlation between cirrhosis and changes in gut microbial composition. There was a significant correlation between the severity of cirrhosis compared to healthy individuals, the displacement of specific GM, and the number of invading microorganisms. However, the causal relationship between GM and cirrhosis and whether inflammatory proteins play a mediating role remain unclear. Therefore, it is necessary to explore the specificity of specific GMs associated with cirrhosis and their underlying inflammatory mechanisms for subsequent risk prediction, treatment, and prognosis of patients with cirrhosis.

Methods

We identified genetic variants closely associated with GM, circulating inflammatory proteins, and cirrhosis from large-scale genome-wide association studies (GWAS) summary data and explored the causal relationship between the three and whether circulating inflammatory proteins mediate the GM-to-cirrhosis pathway using multivariate Mendelian randomization. This study used the inverse variance weighting (IVW) method and MR-Egger as the primary methods, supplemented by the weighted median estimator (WME), the Weighted model, and the Simple model.

Results

There were four positive and three negative results between GM and cirrhosis and five positive and five negative results between circulating inflammatory proteins and cirrhosis. In addition, Tumor necrosis factor ligand superfamily member 12 (TNFSF12) may mediate the Genus Ruminococcus torques-cirrhosis pathway.

Cirrhosis

Gut microbiota (GM)

Inflammatory proteins

Mendelian randomization

Liver-gut axis

Cirrhosis is a chronic, progressive disease characterized by extensive necrosis of liver cells and diffuse hyperplasia of liver fibers. More than 1 million people die each year from cirrhosis and its complications, and cirrhosis is the 11th most common cause of death globally(1).

The gut microbiota is one of the most complex microbial reservoirs in the human body, with more than 3 million coding genes, mainly affecting the body's nutrient absorption, immunity, metabolism, etc(2, 3). It has been shown that gut microorganisms can influence liver metabolism via the liver-gut axis and that dysbiosis of the gut flora may influence the course of liver cirrhosis(3). Simultaneously, the gut microbiota can promote adaptive immunity and regulate inflammatory protein expression to influence parenteral diseases(4). Cirrhosis is an end state of inflammatory disease of the liver(1). The ratio of C-reactive protein to albumin can predict cirrhosis in hepatitis B-associated decompensated cirrhosis, and biomarkers of inflammation can also be used to measure the prognosis of cirrhosis(5, 6). We, therefore, suggest that inflammatory proteins may play a mediating role in the relationship between gut flora and liver cirrhosis. Current studies on the association between gut microbiota and cirrhosis are mostly retrospective(7), susceptible to confounding factors and reverse causality. Most have extracted gut microbes from patients' feces(8). Randomized controlled trials make it difficult to screen the gut flora and alter inflammatory protein levels in patients.

MR analyses use genetic variation as an instrumental variable (IVs) and single nucleotide polymorphisms (SNPs) to assess the causal relationship between exposure and outcome(9). MR is less susceptible to environmental confounders and reverse causation because of the strong correlation between genes, traits, and temporality(10).

In this study, we performed MR analysis and inverse MR analysis of the gut microbiota, 91 inflammatory proteins, and cirrhosis, respectively, to investigate their causal relationship. Multivariate MR analysis was also performed to investigate whether inflammatory proteins play a mediating role from the gut microbiota to cirrhosis.

2.1 Study Design

This study consists of 4 main steps as shown in Figure 1: analysis of the causal effect of the 211 gut microbiota on cirrhosis and reverse causality analysis(Step1); analysis of the causal impact of 91 inflammatory proteins on cirrhosis and reverse causality analysis(Step2); obtain significantly correlated gut microbiota and inflammatory proteins obtained in the first two steps and analyze potential mediating roles(Step3); inclusion of inflammatory proteins and gut microbiota with potential mediating roles in multivariate Mendelian randomization studies(Step4).

The design satisfies Mr's three basic assumptions to ensure the validity of the IVs: (i) the IVs must be strongly correlated with exposure factors; (ii) the IVs affect exposure factors independently and are not related to confounders; (iii) the IVs can only influence outcomes through exposure factors, not through other means(11).

2.2 Data sources

Table 1 shows the summarized GWAS data used for the statistical analysis of the study. Genetic data on the gut microbiota were derived from genome-wide genotypes and 16S fecal microbiome data from 18,340 individuals (24 cohorts) analyzed by the MiBioGen consortium(12).

GWAS summary data for circulating inflammatory proteins were derived from the latest genome-wide protein trait loci (PQTL) study of 91 inflammatory proteins in 14,824 participants(13). GWAS summary data for cirrhosis in this study were obtained from the FinnGen Consortium R11 release, which involves 4869 patients and 448,864 control subjects and is the most recent genome-wide analysis. Detailed disease diagnostic criteria for this dataset can be found at this website(https://risteys.finregistry.fi/endpoints/CIRRHOSIS_BROAD).

The study was an analytical study based on GWAS summary statistics. The GWAS data used in this study were approved by the appropriate Institutional Review Boards and informed consent was provided by the participants.

2.3 Instrumental variables selection

We defined the thresholds for SNPs significantly associated with gut microbiota and inflammatory proteins as P < 1×10 ^− 5 and P < 5×10 ^− 6. To ensure that the selected IVs are authentic and reliable, we have developed the following criteria: (i) The clustering threshold of R² value and allelic distance was specified to be 0.001 and 10,000 kb, respectively, to exclude SNPs in linkage disequilibrium(LD)(14). (ii) Removal of palindromic sequences to exclude allelic effects. (iii) The F statistic for each IV was calculated as

$$\:\text{F}\:=\:(\text{N}-\text{K}-1)/\text{K}\:\times\:\:{\text{R}}^{2}/(1-{\text{R}}^{2})$$

where R², N, and K refer to the estimated exposure variance, sample size, and number of IVs, respectively, and R² was calculated as

$$\:{\text{R}}^{2}\:=\:2\:\times\:\:\text{E}\text{A}\text{F}\:\times\:\:(1-\text{E}\text{A}\text{F})\:\times\:\:{{\beta\:}}^{2}$$

where EAF is the effect allele frequency and β is the allele effect value. SNPs with F < 10 were excluded to avoid the bias caused by weak IVs(15).

2.4 Mendelian randomization analysis

2.4.1 Univariable Mendelian randomization (UVMR) analysis

We performed an MR analysis of the two samples separately to assess the causal effect of gut microbiota and inflammatory proteins on cirrhosis (Steps 1, 2, and 3 in Figure 1). For features containing only one SNP, we use the Wald ratio test, and when the SNPs >2, the inverse variance weighting (IVW) method is used primarily. The MR-Egger regression technique was used to estimate horizontal polytropy, and in the absence of horizontal polytropy, the IVW results were plausible. The Cochran Q statistic is used to measure heterogeneity, and the IVW fixed-effects model is used when there is no heterogeneity. Otherwise, the IVW random-effects model is used(16, 17). The results were statistically significant when the p-value calculated by IVW was less than 0.05, and the IVW was in the same direction as the effect size (β) calculated by MR-Egger.

2.4.2 Multivariable Mendelian randomization (MVMR) analysis

We used both GM and inflammatory proteins, which are significantly associated with cirrhosis, as exposure factors. We used MVMR to validate the potential mediating role of inflammatory proteins in the causal pathway from GM to cirrhosis. UVMR can be obtained separately for the direct effect of GM on cirrhosis in terms of adjusting for the direct impact of mediated post-exposure on outcome and the mediated effect of inflammatory proteins on cirrhosis.

2.4.3 Reverse Mendelian randomization analysis.

To exclude reverse causal interference, we performed MR analyses of cirrhosis to GM and to inflammatory proteins, respectively, using cirrhosis as the exposure outcome. Causality can be further demonstrated when forward MR analysis is statistically significant and reverse MR analysis is not statistically significant.

2.4.4 Sensitivity analysis

To make the results of the study more realistic and reliable, we used a variety of methods to test for heterogeneity and horizontal polytropy. Heterogeneity test: First, we used Cochran's Q test to detect heterogeneity among SNPs, and when the P value was higher than 0.05, it indicated no heterogeneity. We then visualized exposure-related SNPs and outcome-related SNPs to plot leave-one-out plots, which assessed each SNP's impact on the outcome (excluding one SNP at a time and performing IVW analyses on the remaining SNPs)(18). Horizontal multiple validity test: We used the MR-Egger intercept test and Mendelian randomized pleiotropy and outliers (MR-PRESSO) to assess pleiotropy relationships between SNPs and other potential confounders. The MR-Egger intercept test assesses the horizontal multiplicity of SNPs by analyzing the regression intercept and calculating the P-value; if the P-value >0.05, there is no horizontal multiplicity. MR-PRESSO identifies outliers in SNPs and corrects for horizontal pleiotropy by culling outliers(17, 19).

The MR analyses used in this study were performed by R software (version 4.3.3), and the main software packages used included TwoSample MR package (version 0.5.6 https://mrcieu.github.io/TwoSampleMR), MR-PRESSO (version 1.0), q-value (version 2.34.0), Forestploter (version 1.1.1). p<0.05 was considered statistically significant for MR analysis.

3.1 Instrumental variable selection

We identified 195 bacterial traits and 2559 SNPs at the class, family, genus, order, and phylum levels (of which 15 genomes were excluded because the information was unknown and one genome was excluded because there were no eligible SNPs). Detailed GM and the associated SNPs are in Additional file 2: Table S1. The F-statistics for all variables are greater than 10, indicating no weak instrumental variable bias. We identified 1820 SNPs associated with 91 inflammatory proteins, and the F-statistics for all variables were greater than 10(Additional file 3: Table S2).

3.2 UVMR results of GM and inflammatory proteins on cirrhosis

Figure 2 shows the effect of changes in abundance of the 192 GM taxa on the risk of cirrhosis. A total of 7 gut microorganisms (including one order, three families, and three genera) were significantly associated with cirrhosis(Additional file 4: Table S3, Fig. 3). Details of the 89 SNPs for the seven gut microbes are shown in Additional file 5: Table S4. The causal relationship between GM and cirrhosis was also analyzed using IVW, MR-Egger, WME, weighted, and Simple models (Additional file 6: Table S5). The above seven intestinal flora met the IVW analysis with a p-value of less than 0.05 as well as a horizontal pleiotropy test with a p-value of greater than 0.05, and the IVW was in the same direction as the effect size (β) calculated by MR-Egger(Fig. 3). Genetic prediction of 3 GM (family Clostridiaceae1, family Clostridiales vadin BB60 and genus Ruminococcus torques) is associated with reduced risk of cirrhosis. The family Clostridiaceae1 (OR = 0.787, 95%CI = 0.631 ~ 0.981, P = 0.033), family Clostridiales vadin BB60 (OR = 0.818, 95%CI = 0.701 ~ 0.954, P = 0.011), genus Ruminococcus torques (OR = 0.711, 95%CI = 0.536 ~ 0.943, P = 0.018). There are 4 GM that can increase cirrhosis risk: class Melainabacteria (OR = 1.166, 95%CI = 1.006 ~ 1.353 P = 0.042), family Lachnospiraceae (OR = 1.290, 95%CI = 1.044 ~ 1.593 P = 0.018), genus Eubacterium nodatum (OR = 1.121, 95%CI = 1.000 ~ 1.257 P = 0.050), genus Eubacterium ruminantium (OR = 1.162, 95%CI = 1.031~ 1.309 P = 0.014).

Figure 4 shows the effect of 91 circulating inflammatory protein changes on the risk of cirrhosis. A total of 11 inflammatory proteins were significantly associated with cirrhosis(Additional file 7: Table S6). Details of the 205 SNPs for the 11 inflammatory proteins are shown in Additional file 8: Table S7. The causal relationship between inflammatory proteins and cirrhosis was also analyzed using IVW, MR-Egger, WME, weighted, and Simple models (Additional file 9: Table S8). We discarded the T-cell surface glycoprotein CD5 (CD5) because the p-value of the multiplicity test for it was more significant than 0.05. Thus 10 inflammatory proteins were causally associated with cirrhotic episodes at the genetic level (Figure 4). 5 inflammatory proteins negatively correlated with cirrhosis: C-C motif chemokine 4 (CCL4) (OR = 0.923, 95%CI = 0.859 ~ 0.992, P =0.030), CD40L receptor (CD40LR) (OR = 0.915, 95%CI = 0.841 ~ 0.996, P =0.041), Leukemia inhibitory factor receptor (LIFR) (OR = 0.851, 95%CI = 0.760 ~ 0.952, P =0.005), Monocyte chemoatractant protein-3 (MCP-3) (OR = 0.866, 95%CI = 0.764 ~ 0.980, P =0.023), Tumor necrosis factor ligand superfamily member 12 (TNFSF12) (OR = 0.828, 95%CI = 0.733 ~ 0.937, P =0.003). 5 inflammatory proteins positively correlated with cirrhosis: C-X-C motif chemokine 10 (CXCL10) (OR = 1.138, 95%CI = 1.011 ~ 1.281, P =0.032), Fms-related tyrosine kinase 3 ligand (FLT3) (OR = 1.206, 95%CI = 1.084 ~ 1.342, P =0.001), Interleukin-10 receptor subunit alpha (IL10RA) (OR = 1.185, 95%CI = 1.018 ~ 1.380, P =0.029), Interleukin-1-alpha (IL-1α) (OR = 1.220, 95%CI = 1.017 ~ 1.463, P =0.032), Osteoprotegerin (OPG) (OR = 1.252, 95%CI = 1.026 ~ 1.527, P =0.027).

3.3 Sensitivity analysis

According to the MR-Egger regression intercept method and Cochran's Q test, there was no horizontal pleiotropy and heterogeneity of GM significantly associated with cirrhosis; detailed test results can be found in Additional file 10: Table S9 and Additional file 11: Table S10. We discarded T-cell surface glycoprotein CD5 (CD5) based on the MR-Egger regression intercept method that showed horizontal pleiotropy among the inflammatory proteins significantly associated with cirrhosis. The remaining ten inflammatory proteins did not show horizontal pleiotropy(Additional file 12: Table 11). Heterogeneity of Osteoprotegerin (OPG) among the inflammatory proteins significantly associated with cirrhosis according to Cochran's Q test was demonstrated, so we used a randomized IVW approach to explain the causal relationship. The remaining ten inflammatory proteins were not heterogeneous(Additional file 13: Table 12). Significantly associated GM and inflammatory proteins were visualized with cirrhosis as the outcome. The results of the forest, leave-one-out, funnel, and scatter plots were generally consistent with these results(Additional file 1).

3.4 Reverse Mendelian randomization analysis

In the inverse MR analysis of GM to cirrhosis, we set the significance level to P < 1×10-5 and the parameter for removal of linkage disequilibrium (LD) to R² = 0.001, kb = 10,000. Among the 7 GM, the genus Eubacterium ruminantium showed reverse causality with cirrhosis, so we discarded it(The p-value in the IVW analysis was less than 0.05 and in the same direction as the MR-Egger effect size). Detailed results of the MR analysis can be found in Additional file 14: Table 13. We followed the same parametric criteria as the forward MR analysis for reverse MR causality analysis of 10 inflammatory proteins to cirrhosis, and the results showed no reverse causality(Additional file 15: Table 14).

3.5 MVMR results

The GM and inflammatory proteins obtained from the first two steps in Fig. 1 were used as exposures and endpoints for MR analysis of GM to inflammatory proteins(Fig. 1, Step 3). We analyzed the causal relationship between GM and inflammatory proteins described above and obtained eight groups of inflammatory proteins with possible mediating roles(Additional file 16: Table 15). Class Melainabacteria and TNFSF12, LIFR and MCP-3 all showed positive correlations(OR=1.104, 95%CI =1.019~ 1.196, P=0.016)、(OR=1.096, 95%CI =1.005~ 1.195, P=0.038)、(OR=1.101, 95%CI =1.004~ 1.210, P=0.041). Family Clostridiaceae1 and IL10RA, CCL4 and MCP-3 all showed negative correlation(OR=0.838, 95%CI =0.711~ 0.987, P=0.035)、(OR=0.866, 95%CI =0.760~ 0.987, P=0.032)、(OR=0.857, 95%CI =0.742~ 0.990, P=0.037). Family Lachnospiraceae and IL-1α are negatively correlated(OR=0.882, 95%CI =0.786~ 0.990, P=0.033). Positive correlation between Genus Ruminococcus torques and TNFSF12(OR=1.176 95%CI =1.001~ 1.383, P=0.049).

We sequentially included eight pairs of GM and inflammatory proteins associated with cirrhosis in mediation analyses using the MVMR method. We calculated the indirect effects (b') and proportions (c × b'/a) mediated by these inflammatory proteins. As shown in Table 2, After adjusting for Genus Ruminococcus torques, TNFSF12 still showed a significant correlation with cirrhosis, in contrast to Genus Ruminococcus torques, whose effect on cirrhosis became non-significant under multivariate adjustment(P=0.097). Therefore, we suggest that TNFSF12 affects the correlation between Genus Ruminococcus torques and cirrhosis and that TNFSF12 may mediate the Genus Ruminococcus torques to cirrhosis pathway. The mediation percentage was 6.8% (P=0.023)

The human gut contains approximately 1.5 kilograms of cells, most of which are bacteria, which play an essential role in the dynamic balance of physiology and metabolism(20, 21). The portal vein is the primary connection between the intestinal environment and the liver. At the same time, the bile ducts also serve as a direct and immediate conduit between the gastrointestinal tract and the liver so that substances in the intestines can be transported quickly to the liver. Dynamic interactions between the liver and gut are based on mechanisms such as biliary excretion and antibody release(22). This complex interaction between the liver and intestine is called the liver-gut axis. Several studies have shown a significant correlation between cirrhosis and changes in the composition of gut microbes(23–26). The considerable displacement of the gut microbiota in patients with cirrhosis compared to healthy individuals and the significant correlation between the abundance of invading microorganisms and the severity of the disease suggests that there is a relationship between these displaced microorganisms that may be involved in the underlying pathomechanisms of the disease. Correcting gut microbial imbalances may provide new perspectives for the treatment of cirrhosis(27).

In this study, we found a negative correlation between Ruminococcus torques and liver cirrhosis (OR = 0.711, 95%CI = 0.536 ~ 0.943, P = 0.018). One of the first stomach bacteria to be discovered, Ruminococcus torques, plays a vital role in metabolism. Rumenococcus obtains nutrients by breaking down cellulose in the host's digestive system and is also capable of fermenting glucose and xylose(28). A study found that rumenococci were significantly associated with intrahepatic cholestasis in pregnancy and were protective factors for the disease(OR: 0.448, 95%CI༚0.235 ~ 0.854, P = 0.015)(29). In a study of the diversity of the intestinal flora in patients with cirrhotic portal hypertension a large reduction of Clostridiales and Rumenococcus was found, which is consistent with our results(30). However, the study of ruminococcus torques in cirrhosis is still insufficient, and more experiments are needed to investigate its function in the liver-gut axis.

Tumor necrosis factor ligand superfamily member 12 is a tumor necrosis factor-like weak inducer of apoptosis (TNFSF12/TWEAK). TNFSF12 triggers downstream signalling to regulate key events associated with tumour inflammation, angiogenesis and epithelial mesenchymal transition(31, 32). TNFSF12 regulates angiogenesis by inducing apoptosis through various pathways and promoting endothelial cell proliferation and migration. In chronic injury, over-activated TNFSF12 pathways can promote pathological tissue changes, including chronic inflammation, fibrosis, and angiogenesis(31). Fibroblast growth factor-inducible molecule 14 (Fn14) is the only functional receptor for TNFSF12, and TNFSF12/Fn14 plays a beneficial role in tissue repair after acute injury(31, 33). However, in chronic liver disease, increased TWEAK expression promotes the proliferation of hepatic stellate cells, a key regulator of liver fibrosis(34).

We found that when both Ruminococcus torques and Tumor necrosis factor ligand superfamily member 12 (TNFSF12) were included for multivariate MR analysis, the P-value of Ruminococcus torques changed considerably from 0.018 to 0.097 compared with the previous(Fig. 1, Step 1,4), suggesting a more pronounced mediating effect of TNFSF12. However, the calculated share of mediation effects ( c×b'/a) is not very high. We believe that this may be because the number of cases in the data we chose was too small, whereas the control group had a large sample size.

This study is the first large MR study to use multivariate MR analysis to investigate the mediating role of circulating inflammatory proteins in the GM to cirrhosis causality chain. However, this study has some limitations: first, our study only used a European population as a sample. In the future, we should collect sample data from populations of different backgrounds to verify whether the causal relationships found in this study can be generalized. In addition, GM abundance may be affected by diet, gender, medication, lifestyle, and sampling time, and the degree of variation explained by genes may be reduced. Third, limited by the small number of cases in this dataset, we did not accurately categorize cirrhosis for its etiologies, such as viral, alcoholic, autoimmune, and cholestatic cirrhosis. Subsequent studies should further investigate cirrhosis with a clear etiology to obtain a more specific causal relationship. Although we investigated the mediating role of circulating inflammatory proteins in the causal chain from GM to cirrhosis, the mechanisms of how specific inflammatory proteins influence the development of cirrhosis remain to be investigated.

This study comprehensively explored the causal relationships between GM, inflammatory proteins, and cirrhosis. There were four positive and three negative causal relationships between GM and cirrhosis. There were five positive and five negative causal relationships between inflammatory proteins and cirrhosis. In addition, Tumor necrosis factor ligand superfamily member 12 (TNFSF12) may mediate the Genus Ruminococcus torques-cirrhosis pathway. These findings support the scientific basis for GM to contribute to the development of cirrhosis through the gut-liver axis and provide potential targets for inflammatory proteins such as cirrhosis prevention, diagnosis, and treatment. However, this study needs to further dissect the pathophysiological effects of inflammatory proteins in specific GM on patients with cirrhosis of different etiologies.

GM: gut microbiota; MR: Mendelian randomization; IVs: instrumental variables; SNPs: single nucleotide polymorphisms; GWAS: genome-wide association studies; UVMR: Univariable Mendelian randomisation; pQTL: protein quantitative trait locus; LD: Linkage disequilibrium; IVW: inverse variance weighted; MR-Egger: Mendelian randomization of Egger regression; WME: weighted median estimator; ORs: odds ratios; CIs: confidence intervals; SE: status epilepticus; MVMR: Multivariable Mendelian randomization; MR-PRESSO: Mendelian Randomization Pleiotropy RESidual Sum and Outlier; CCL4: C-C motif chemokine 4; CD40LR: CD40L receptor; LIFR: Leukemia inhibitory factor receptor ;MCP-3: Monocyte chemoatractant protein-3; TNFSF12: Tumor necrosis factor ligand superfamily member 12 ; CXCL10: C-X-C motif chemokine 10; FLT3: Fms-related tyrosine kinase 3 ligand; IL10RA: Interleukin-10 receptor subunit alpha; IL-1α: Interleukin-1-alpha ; OPG: Osteoprotegerin; Fn14: Fibroblast growth factor-inducible molecule 14;

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Availability of data and materials

GWAS summary data for GM genera from the MiBioGen repository was available at https://mibiogen.gcc.rug.nl/. GWAS datasets for 91 circulating inflammatory proteins are availablefrom the EBI GWAS Catalog (https://www.ebi.ac.uk/gwas/downloads/summary-statistics/ accession numbers GCST90274758 to GCST90274848). GWAS data for cirrhosis from the FinnGen consortium were available at https://r11.finngen.fi/.

Competing Interests

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Funding

The study was supported by the KeyR&D Project in Shaanxi Province of the Second Affiliated Hospital of Xi'an Jiaotong University.(2022SF-372)

Author Contributions

QFL, HL conceptualized the study design. QFL, HL, HY,YXG, and SQA acquired and analyzed the statistical data. QFL, CFW visualized the statistical data. HY, SFD, SQA, CFW interpreted the results and wrote the manuscript. SFD, SQA, CFW edited and reviewed the manuscript. AJ provided research funding, guided and revised crucial intellectual content of the manuscript. All authors provided feedback on revisions, read and approved the final report.

Acknowledgments

We want to thank the participants and researchers in the FinnGen study and the MiBioGen consortium as well as researchers such as Zhao JH and Stacey D, for sharing the GWAS summary statistics. We would like to express our sincerest gratitude to all the researchers and participants of the above databases.

STROBE-MR guidelines

The manuscript complies with the STROBE-MR guidelines

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Table 1 Characteristics of GWAS summary data included in this study

Trait Consortium Samples Case Control

Exposure

211 GM taxa MiBioGen 18,340 \ \

91 circulating EBI GWAS 14,824 \ \

Inflammatory Catalog

proteins

Outcome

Cirrhosis FinnGen (R11) 453,733 4,869 448,864

Table 2. Mediating role of inflammatory proteins in microbiota to cirrhosis

Exposure	Mediator	Direct effect β(b'±SE)	P1	Direct effect β(a'±SE)	P2	Mediation effect (c×b'±SE)	Mediated Proportion (c×b'/a)
Genus Ruminococcus torques	TNFSF12	-0.142±0.063	0.023	-0.236±0.142	0.097	-0.023±0.016	0.068
Class Melainabacteria	LIFR	-0.205±0.061	0.001	0.205±0.065	0.002	-0.019±0.011	-0.012
	MCP3	-0.118±0.068	0.082	0.169±0.063	0.008	-0.011±0.009	-0.073
	TNFSF12	-0.148±0.063	0.020	0.176±0.071	0.013	-0.015±0.009	-0.094
Family Clostridiaceae1	IL10RA	-0.014±0.073	0.849	-0.240±0.092	0.009	0.002±0.014	0.016
	CCL4	-0.077±0.029	0.007	-0.288±0.082	<0.001	0.011±0.007	-0.046
	MCP3	-0.081±0.074	0.274	-0.239±0.118	0.042	0.012±0.014	-0.052
Family Lachnospiraceae	IL1	0.085±0.081	0.295	0.278±0.099	0.005	-0.011±0.012	-0.042

Both a' and b' are from multivariate MR analyses. b' represents the effect value of inflammatory proteins to cirrhosis, and a' represents the direct effect value of gut microbiota to cirrhosis. c is derived from Step3 of Figure 1, and represents the effect value of intestinal flora to inflammatory proteins. c × b' represents the mediating effect of inflammatory proteins. a is derived from Step1 of Figure 1, and represents the total effect of intestinal flora to cirrhosis. c × b '/a represents the percentage of effect mediated by inflammatory protein as a mediator.

No competing interests reported.

Gut microbiota, circulating inflammatory proteins, and cirrhosis: a multivariable Mendelian randomization study

Status:

Version 1

Abstract

Background

Methods

Results

Figures

Introduction

Methods

2.1 Study Design

2.2 Data sources

2.4.1 Univariable Mendelian randomization (UVMR) analysis

2.4.2 Multivariable Mendelian randomization (MVMR) analysis

2.4.3 Reverse Mendelian randomization analysis.

2.4.4 Sensitivity analysis

Result

Discussion

Conclusion

Abbreviations

Declarations

References

Tables

Additional Declarations

Supplementary Files

Status:

Version 1