In recent decades, studies have demonstrated that the entire gastrointestinal tract has a remarkable number of commensals with different functions in the pathophysiology of many intestinal and extraintestinal diseases (Tilg et al., 2022). Any perturbations between host and microbe interaction will upset the balance and cause diseases. Microbes and the liver mutually influence each other through metabolites and signals produced by dietary, genetic, and environmental factors, described as the Gut-Liver Axis (Albillos et al., 2020).

Cirrhosis is regarded as the end-stage of liver diseases and can cause portal hypertension and esophageal varices that lead to death (Tsochatzis et al., 2014). It is the 11th most common cause of death worldwide, and about 1 million people die every year due to cirrhosis (Gines et al., 2021). Many risk factors can lead to cirrhosis, including viruses, obesity, and alcohol consumption (Gines et al., 2021). However, a non-negligible factor is the microbes in the gastrointestinal tract (Tripathi et al., 2018). A complication of cirrhosis, portal hypertension, can damage the gastrointestinal mucosal barrier (Tilg et al., 2022). This damage unbalances the Gut-liver axis. The composition of intestinal flora changes, and then the secretion of inflammatory factors increases, which eventually leads to serious complications that have a significant impact on the treatment and prognosis of patients with liver cirrhosis (Chen et al., 2011; Bajaj et al., 2012; Sole et al., 2021). Gut flora is also a risk factor for hepatic encephalopathy (HE) in patients with cirrhosis (Vilstrup et al., 2014). However, current guidelines do not recommend the use of antibiotics or probiotics for the prevention of HE, especially in patients who have undergone trans-jugular intrahepatic portosystemic shunt (TIPS) (Gairing et al., 2022). This is because studies at this stage have produced conflicting results, and there is still a lack of large-scale randomized control trials (RCTs) to prove the benefit (Gairing et al., 2022). Furthermore, the gut microbiota is also susceptible to a variety of other factors, such as diet, the environment, and obesity (Tilg et al., 2022). Traditional methods, such as RCT, cannot solve the paradox of the interaction between bacteria and confounding factors by design, and thereby reduce the credibility of RCT (Vujkovic-Cvijin et al., 2020).

Mendelian randomization (MR) is a genetic epidemiological method that uses instrumental variables (IVs), strongly associated with exposure, to study causality and deal with confounding factors (Emdin et al., 2017; Davies et al., 2018). It is based on the fact that Single nucleotide polymorphisms (SNPs) are randomly variated and distributed during gamete formation, and it is not affected by confounding factors after gametogenesis. Since SNPs are not influenced by the outcome, we could exclude potential reverse causality effects (Bowden et al., 2015). MR, as a novel method, could address confounding factors and reverse causality by using genetic variants strongly associated with exposure to infer causality with outcome (Emdin et al., 2017; Davies et al., 2018).

In this study, we investigated the potential causal effect between 211 gut microbiota taxa and several subtypes of cirrhosis, including cirrhosis (ICD-10: K74), primary biliary cirrhosis (PBC), and alcoholic cirrhosis (ALC), by MR study design. Our study provided a new insight into the relationship between liver cirrhosis and gut microbiota.

In this study, we analyzed 211 microbiotas in the gut by bi-directional two sample MR analysis to detect the potential causal association between gut microbiota and cirrhosis. Previous literature suggested that Lachnospiraceae was beneficial for liver cirrhosis (Family Lachnospiraceae) (Chen et al., 2011; Bajaj et al., 2014, 2021). However, Lachnospiraceae, in our study, was considered as a risk factor for cirrhosis. This discovery was also shown in the process of exploring gut microbiota and ALC (Genus Lachnospiraceae NK4A136 group). Currently, no randomized controlled trials (RCTs) have directly substantiated the protective impact of Lachnospiraceae on liver cirrhosis. So far, only a change in the abundance of Lachnospiraceae has been detected prior to fecal transplantation (Bajaj et al., 2021). This observation implies that the indigenous microbiota may confer a safeguarding influence on Lachnospiraceae. However, a definitive deduction regarding Lachnospiraceae as a protective factor for liver cirrhosis cannot be established. Furthermore, it is noteworthy that Lachnospiraceae encompasses multiple distinct species that potentially assume diverse roles in the context of cirrhosis. In this regard, we put forward a hypothesis: Lachnospiraceae can lead to the occurrence of liver cirrhosis. In the state of cirrhosis, the proliferation rate of some intestinal flora was accelerated, resulting in the inhibition of the growth of Lachnospiraceae. We maintained that cirrhosis was not responsible for the inhibition of Lachnospiraceae growth, as this possibility was formally absent from the reverse MR analysis.

Bifidobacteriaceae are involved in the metabolism of short-chain fatty acids and bile acids; they were, thus, considered as beneficial bacteria (Ridlon et al., 2013; Riviere et al., 2016). In the reverse MR analysis, we found that cirrhosis (K74) is a risk factor for Bifidobacteria at the levels of Genus, Family, and Order (Family Bifidobacteriaceae, Genus Bifidobacterium, and Order Bifidobacteriales). We did not find a protective effect of Bifidobacterium on cirrhosis. For a long time, Bifidobacterium has been considered as a kind of beneficial bacteria for liver cirrhosis. An animal study revealed a marked decrease in the abundance of Bifidobacterium during the pathophysiology of fat-associated cirrhosis and liver cancer induced by high-cholesterol diet (Zhang et al., 2021). A clinical trial showed that Bifidobacterium can promote the transformation of macrophages and control the inflammatory response in patients with liver cirrhosis (Moratalla et al., 2016). However, our results showed that Bifidobacterium did not have a protective effect on liver cirrhosis but reduced in the cirrhotic state.

Furthermore, we found that Genus Erysipelatoclostridium was associated with a lower risk of cirrhosis (K74) and PBC. It has been observed that enrichment of Faecalibacterium was positively associated with Erysipelatoclostridium in healthy infants but not in cholestatic infants (Guo et al., 2018). Our findings suggested that there might be a direct effect of Genus Erysipelatoclostridium in improving the cirrhotic status of patients. Class Coriobacteriia, Family Coriobacteriaceae, and Order Coriobacteriales were regarded as risk factors for ALC in our study. These floras were the same as in the cirrhosis results (K74). However, there was no literature to report the causal effect of these microbiotas on cirrhosis. It is intriguing that Coriobacterium was considered a protective factor for type 2 diabetes due to its involvement in the metabolism of glutamate, but it was a risk factor for cirrhosis in our study (Zhuang et al., 2021). This indicates that the same microbiome plays different roles in different diseases.

In addition, it is worth noting that there are no reported studies on the causal effect of Genus Butyricicoccus on PBC. Distinctively, our study firstly demonstrated a bidirectional causal association between Genus Butyricicoccus and PBC. This association should be further investigated, and it may be a new target for PBC therapy.

Furthermore, some novel causal relationships were discovered in the present study. We found that Class Alphaproteobacteria and Genus Holdemanella, had a causal effect on the pathogenesis of cirrhosis (K74). There were several different taxa for ALC that could be risk factors, such as Genus Fusicatenibacter, Order Gastranaerophilales. Family Victivallaceae were a protective factor for ALC. In reverse MR analysis, Class Actinobacteria and Genus Prevotella9 were inhibited in cirrhosis, but Phylum Cyanobacteria liked this environment. Some bacterial groups could cause the occurrence of PBC (Class Mollicutes, Genus Ruminococcus gauvreauii, Genus Dorea, Order Rhodospirillales, and Phylum Tenericutes). The causal relationship between PBC and these bacterial groups, however, was not observed in the reverse MR analysis. Genus Clostridium innocuum were beneficial for PBC in the MR analysis. In reverse MR analysis, Family Actinomycetaceae, Genus Actinomyces, and Order Actinomycetales were negatively affected under the condition of PBC, but Genus Barnesiella was positive under that. Further research is needed to confirm these causal relationships.

Our study has certain limitations. First, restricted by GWAS data, we can only accurately study the relationship between genus or above level and liver cirrhosis, but not validate a further specific level, such as species. Second, due to unavailable access to other GWAS data of ALC and the small size of IVs, we could not perform reverse MR analysis in ALC. Third, we did not analyze the relationship between viral cirrhosis, syphilitic cirrhosis, and other rare types of cirrhosis and gut microbiota. Fourth, we did not correct the P-value of IVW to control the Type I error rate. Due to the use of public GWAS data, the number of case and control groups in these cohorts was imbalanced, which may increase the likelihood of pleiotropy. Meanwhile, the effect of genetic mutations on other pathways may also affect the feasibility of the results. Finally, due to the lack of relevant GWAS data, we did not perform validation through replication samples.

In conclusion, we analyzed the causality between 211 gut microbiotas and cirrhosis. We demonstrated that 21 gut microbiotas might be risk or protective factors for cirrhosis, and 11 gut microbiotas might be influenced under the condition of cirrhosis.

The original contributions presented in the study are included in the article/Supplementary material, further inquiries can be directed to the corresponding authors.

Q-AX: Formal analysis, Investigation, Methodology, Writing—original draft. Y-FY: Writing—original draft, Data curation. LC: Writing—original draft, Formal analysis. Y-CX: Investigation, Methodology, Software, Writing—review and editing. H-TL: Investigation, Methodology, Resources, Writing—review and editing. Z-GF: Investigation, Methodology, Software, Writing—review and editing. QH: Investigation, Methodology, Software, Writing—review and editing. JQ: Investigation, Methodology, Formal analysis, Writing—review and editing. JT: Formal analysis, Investigation, Data curation, Writing—review and editing. W-JZ: Data curation, Formal analysis, Investigation, Writing—review and editing. FC: Investigation, Methodology, Software, Writing—review and editing. Y-TH: Writing—review and editing. L-FA: Investigation, Resources, Software, Supervision, Writing—review and editing. CL: Resources, Software, Methodology, Visualization, Writing—review and editing. X-YC: Software, Formal analysis, Investigation, Writing—review and editing. DW: Supervision, Validation, Visualization, Writing—review and editing. Y-YT: Supervision, Validation, Visualization, Writing—review and editing, Project administration. XX: Project administration, Supervision, Validation, Visualization, Writing—review and editing, Funding acquisition, Resources. X-LZ: Funding acquisition, Project administration, Resources, Supervision, Validation, Visualization, Writing—review and editing.