The Causal Relationship between Gut Microbiota and Non-alcoholic Fatty Liver Disease: A Mendelian Randomization Study

doi:10.21203/rs.3.rs-3228738/v1

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The Causal Relationship between Gut Microbiota and Non-alcoholic Fatty Liver Disease: A Mendelian Randomization Study

https://doi.org/10.21203/rs.3.rs-3228738/v1

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Aims

The association between the gut microbiota and non-alcoholic fatty liver disease (NAFLD) has been documented; however, the causal relationship between them remains unclear. The aim of this study was to explore the causal relationship between the gut microbiota and NAFLD using Mendelian randomization.

Methods

We conducted a Mendelian randomization study, using gut microbiota data (n = 18340) from MiBioGen consortium as the exposure and the NAFLD dataset from FinnGen R9 release data (n = 377,277) as the outcome. Inverse variance weighted was employed as the primary analysis method, and sensitivity analysis was performed. Additionally, a network graph resembling a phylogenetic tree was created to further reveal the evolutionary relationships among gut microbiota.

Results

Six bacterial features exhibited causal relationships with NAFLD: Actinomycetales (OR = 1.50, 95% CI: 1.01–2.21, p = 0.043), Actinomycetaceae (OR = 1.49, 95% CI: 1.01–2.20, p = 0.043), Actinomyces (OR = 1.36, 95% CI: 1.09–1.71, p = 0.006), Prevotella 7 (OR = 1.19, 95% CI: 1.01–1.39, p = 0.039) exhibited potential detrimental effects on NAFLD, whereas Anaerofilum (OR = 0.85, 95% CI: 0.72–0.99, p = 0.042) and Gordonibacter (OR = 0.84, 95% CI: 0.72–0.98, p = 0.024) exhibited potential protective effects. Furthermore, Actinomycetales, Actinomycetaceae, and Actinomyces are located on the same evolutionary branch.

Conclusions

Our study revealed a causal relationship between six gut bacteria and NAFLD. These findings shed light on the role of specific gut bacteria in the pathogenesis of NAFLD and offer valuable insights into future therapeutic interventions and preventive strategies.

non-alcoholic fatty liver disease

gut microbiota

Mendelian randomization

Non-alcoholic fatty liver disease (NAFLD) is a prevalent metabolic disorder that affect approximately 25% of the global population. ¹ It serves as a risk factor for various diseases, including diabetes, hypertension, cardiovascular diseases, and chronic kidney disease. ² Serious complications, including liver fibrosis and hepatocellular carcinoma, can be caused by advanced-stage NAFLD, thereby imposing a substantial burden on society and the economy. Despite extensive research, the precise etiology of NAFLD remains unclear. Recent studies have indicated that the pathogenesis of NAFLD involves factors such as insulin resistance, environmental factors, obesity, dysbiosis of the gut microbiota, predisposing genetic variants, and other contributing factors. ³ Notably, emerging evidence has highlighted the potential impact of the gut microbiota on the development of NAFLD. ⁴

Microorganisms are ubiquitous in nature, with commensal microbes inhabiting various parts of the human body, including the oral cavity, skin, urogenital tract, and, importantly, gastrointestinal tract. The gut microbiota, often referred to as the "second genome" of humans, plays a pivotal role in facilitating nutrient digestion and absorption, while actively participating in host metabolism. Research has demonstrated that the gut microbiota can ameliorate insulin resistance, ⁵ underscoring its regulatory role in human metabolism. NAFLD is a disease caused by metabolic disturbances and is associated with insulin resistance, suggesting a potential association between gut microbiota and NAFLD. Patients with NAFLD exhibit lower diversities in their gut microbiota than healthy individuals. ⁶ However, the causal relationship between the gut microbiota and NAFLD remains uncertain, and further investigation is needed to determine whether changes in the gut microbiota contribute to the development of NAFLD or whether NAFLD itself leads to alterations in the gut microbiota.

Mendelian randomization studies have increasingly been employed for causal inferences in cross-sectional data. Genetic variations, which undergo random allocation before birth ⁷ and remain unaffected by postnatal environmental influences, serve as valuable resources for inferring causal relationships between exposure factors (e.g., gut microbiota) and outcome variables (e.g., NAFLD). In comparison to observational studies, Mendelian randomization not only assesses the association between exposure and outcome but also enables causal inference between them. Unlike randomized controlled trials, Mendelian randomization utilizes existing data for causal analysis, reducing the human and material costs associated with conducting randomized controlled trials. Therefore, in the present study, we utilized the largest currently available gut microbiota genome-wide association study (GWAS) database developed by the MiBioGen consortium (https://mibiogen.gcc.rug.nl/) and the FinnGen database (https://www.finngen.fi/fi), conducting a two-sample Mendelian randomization analysis to evaluate the causal relationship between gut microbiota and NAFLD.

Data Source

The FinnGen project, initiated in 2017, is a collaborative effort between Finnish universities, research institutions, biomedical companies, and hospitals. The objective was to collect biological samples, genetic information, medical records, and lifestyle data from approximately 500,000 Finnish individuals, which represents approximately one-tenth of the country's total population. ⁸ The GWAS summary data used for NAFLD in our study were obtained from the FinnGen consortium's R9 release data. The file "finngen_R9_NAFLD.gz" can be downloaded from the link provided by FinnGen (https://storage.googleapis.com/finngen-public-data-r9/summary_stats/finngen_R9_NAFLD.gz). This dataset comprises 2,275 cases and 375,002 controls.

The human gut microbiome GWAS data used in our study were obtained from the MiBioGen consortium (https://mibiogen.gcc.rug.nl/). MiBioGen is a GWAS project that integrates data from 24 cohorts comprising 18,340 individuals of diverse ethnicities and nationalities from Europe, America, the Middle East, and East Asia. To date, this represents the largest GWAS effort focusing on the human gut microbiota, employing 16S rRNA amplicon sequencing to target the V1-V2, V3-V4, and V4 regions. The database comprises 211 bacterial features, including 9 phyla, 16 classes, 20 orders, 35 families, and 131 genera. Among these, 15 bacterial features remained unclassified. Owing to the absence of taxonomy for these bacteria and the limited understanding of their functionality, they were excluded from the subsequent analysis.

The informed consent procedures have been described in the original publications of these studies.

Instrumental variable selection

In this study, we considered the gut microbiome to be an exposure factor. Instrumental variables (IVs) associated with NAFLD were selected using a p-value threshold of 1×10^− 5. We used data from the 1000 Genomes Project European samples as a reference panel to calculate linkage disequilibrium. Only SNPs with R² < 0.001 and clump window size < 10,000 kb were retained. In cases where palindromic SNPs were present, the Harmonise function was used to remove them prior to subsequent analyses (Table S1). The instrumental variable selection process was performed using R software v4.3.1 and the TwoSampleMR package.

Statistical analysis

Mendelian randomization (MR) analysis was conducted using six methods: inverse variance weighted (IVW), inverse variance weighted with multiplicative random effects (IVW-MRE), MR Egger, Maximum likelihood (ML), weighted median, and Mendelian randomization pleiotropy residual sum and outlier (MR-PRESSO). IVW is a meta-analysis method that has been used as the primary analysis approach to assess the association between the gut microbiome and NAFLD. However, this method assumed no heterogeneity or horizontal pleiotropy. The IVW-MRE considers potential heterogeneity among the Wald ratio estimates from SNPs, providing a reliable approach to evaluate causal relationships when heterogeneity is present. ⁹ If heterogeneity exists, our results will be presented using the IVW-MRE method. The MR Egger detects horizontal pleiotropy based on the intercept term, where an intercept of zero indicates no horizontal pleiotropy. ML is similar to IVW but yields smaller standard errors if no horizontal pleiotropy is present. ¹⁰ Weighted median allows for up to 50% of invalid IVs and still estimates causal effects. ¹¹ MR-PRESSO is employed to detect and remove significant outliers, reducing horizontal pleiotropy. ¹²

To assess heterogeneity among instrumental variables, we employed Cochran's IVW Q statistics and considered a p-value < 0.05 as indicative of heterogeneity. F-statistics were used to evaluate the instrumental variable strength, where F-statistics = beta²/se², with F < 10 regarded as weak IVs. ¹³ Leave-one-out analysis was conducted to identify individual SNPs with significant causal effects on the outcome. The Steiger test was used to determine the directionality of the causal relationship between the gut microbiome and NAFLD. The phylogenetic tree serves as a powerful tool for unveiling the evolutionary relationships among bacteria, highlighting the functional similarities among closely related bacteria. Regrettably, the absence of accessible bacterial genome information precludes reconstruction of the phylogenetic tree in this study. Instead, a network graph resembling a phylogenetic tree was manually constructed by querying the SILVA (https://www.arb-silva.de/) and EMBL-EBI/ENA (https://www.ebi.ac.uk/ena/browser/home) databases, and we name it “phylogenetic-like plot”.

All data analyses were performed using R software v4.3.1, and the R packages TwoSampleMR, ggplot2, dplyr, MRPRESSO, grid, and forestploter.

NAFLD-Associated Gut Microbiota Feature Selection

A total of 211 bacterial features from the gut microbiota GWAS data obtained from MiBioGen were analyzed. We identified 24 gut microbiota features (Fig. 1 and Table S2) that were potentially associated with NAFLD, including 3 phyla (Figure S1), 3 classes (Figure S2), 2 orders (Figure S3), 4 families (Figure S4), and 12 genera (Figure S5). Notably, one genus remained unclassified, and owing to its unclear functionality, it was excluded from the subsequent analyses.

Sensitivity Analysis Reinforces the Robustness of Causal Relationship Between Gut Microbiota and NAFLD

To ensure the robustness of our findings, we assessed the strength of the IVs, and conducted sensitivity analyses to evaluate the heterogeneity and horizontal pleiotropy. No evidence of bias from weak IVs was identified among the selected IVs (F-statistic > 10, Table S2). Cochran's IVW Q statistics indicated no significant heterogeneity (p > 0.05, Table S2) among the IVs selected for Actinomyces, Anaerofilum, Gordonibacter, Prevotella 7, and Turicibacter. Additionally, both the MR Egger intercept (p > 0.05, Fig. 3 and Table S2) and MR-PRESSO global test (p > 0.05, Table S3) provided insufficient evidence of horizontal pleiotropy for Actinomycetales, Actinomycetaceae, Actinomyces, Anaerofilum, Gordonibacter, and Prevotella 7. Leave-one-out analysis demonstrated no individual SNPs driving the association between the gut microbiota and NAFLD (Fig. 2). We observed heterogeneity among the majority of the bacteria; however, heterogeneity was expected in such a complex system. Therefore, bacterial features displaying horizontal pleiotropy were excluded from the subsequent analysis. Following this exclusion, a final set of six bacterial features was identified, revealing potential causal associations with NAFLD and no evidence of horizontal pleiotropy. These features were defined as “key gut microbiota”, which included Actinomycetales, Actinomycetaceae, Actinomyces, Prevotella 7, Anaerofilum and Gordonibacter (Fig. 3).

Causal Effects of the Gut Microbiota on NAFLD

To further investigate the causal relationship between key gut microbiota and NAFLD, we performed Mendelian randomization analysis. The key gut microbiota including Actinomycetales (OR = 1.50, 95% CI: 1.01–2.21, p = 0.043), Actinomycetaceae (OR = 1.49, 95% CI: 1.01–2.20, p = 0.043), Actinomyces (OR = 1.36, 95% CI: 1.09–1.71, p = 0.006), and Prevotella 7 (OR = 1.19, 95% CI: 1.01–1.39, p = 0.039) were associated with potential detrimental effects on NAFLD, suggesting their involvement in the pathogenesis of this disease. Conversely, Anaerofilum (OR = 0.85, 95% CI: 0.72–0.99, p = 0.042) and Gordonibacter (OR = 0.84, 95% CI: 0.72–0.98, p = 0.024) exhibited protective effects against NAFLD, indicating their potential therapeutic relevance (Fig. 4 and Table S2). To assess the directionality of the causal effects, a Steiger test was conducted, revealing that all causal effects were positive (Steiger test, p < 0.05, Table S4).

Relationships Among Gut Microbiota Revealed by Phylogenetic-like Plot

To gain further insight into the potential synergistic interactions among the identified gut microbiota associated with detrimental or protective effects on NAFLD, we conducted a phylogenetic-like plot using the gut bacteria included in the current study. Interestingly, the phylogenetic-like plot illustrated that the order Actinomycetales, the family Actinomycetaceae, and the genus Actinomyce are located on the same evolutionary branch (Fig. 5). This observation suggests a close relationship and functional similarity between these three bacterial features, underscoring their crucial roles in the development and progression of NAFLD.

In this study, we investigated the causal relationship between gut microbiota and NAFLD using the FinnGen R9 release data and the MiBioGen dataset. Our findings revealed that Actinomycetales, Actinomycetaceae, and Actinomyces were associated with an increased risk of NAFLD and exhibited monophyletic clustering in the phylogenetic-like plot (Fig. 5). Given their functional similarities, we have focused on our discussion at the genus level. In addition, Prevotella 7 demonstrated detrimental effects on NAFLD, whereas Anaerofilum and Gordonibacter exhibited protective effects against NAFLD.

Several studies have highlighted the association between the gut microbiota and NAFLD. ¹⁴ Mechanisms underlying the promotion of NAFLD by dysbiosis of gut microbiota include (i) altered energy harvest and reduced microbial diversity; (ii) elevated levels of branched-chain and aromatic amino acids; (iii) increased production of metabolites such as phenylacetic acid and ethanol by microbes, leading to hepatic lipid accumulation in vitro and in vivo; and (iv) elevated levels of microbial endotoxins, potentially contributing to inflammation. ¹⁵

Branch-chain amino acids (BCAAs) and aromatic amino acids (AAAs) are the fundamental building blocks of polypeptide chains that play crucial roles in human metabolism. Elevated levels of BCAAs, including leucine, isoleucine, and valine, have been found to be associated with increased insulin resistance, ¹⁶ which is an important pathogenic mechanism in NAFLD. Conversely, depletion of isoleucine levels triggers an augmentation of hepatic metabolism by upregulating the pathways associated with glycolysis and lipid metabolism, resulting in notable enhancements in the metabolic functions of the liver. Additionally, low levels of isoleucine can promote energy expenditure via the FGF21-UCP1 pathway. ¹⁷ The group AAAs includes phenylalanine, tyrosine, and tryptophan. Tryptophan directly participates in the immune responses in liver diseases. ¹⁸ Phenylacetic acid is a metabolic product of phenylalanine, and has been found to trigger hepatic lipid accumulation and steatosis. ¹⁹ Besides, AAAs have been implicated in metabolic disorders such as coronary atherosclerosis and acute myocardial infarction, primarily exerting their effects through alterations in lipid metabolism. ²⁰ The abundance of Actinomyces exhibited a positive correlation with the levels of BCAAs and AAAs, which were found to be higher in the fecal samples of NAFLD patients, resulting in elevated levels of branched-chain and aromatic amino acids. ²¹ These findings suggest a potential contribution of Actinomyces to the development of NAFLD, which are consistent with our results.

Prevotella 7, a Gram-negative bacterium, is likely involved in the mechanism leading to NAFLD through the production of lipopolysaccharides (LPS). ²² The intestinal mucosal barrier can be damaged by LPS, which is the most prevalent bacterial endotoxin. ²³ This damage results in an increase in endotoxemia levels and the subsequent initiation of chronic liver inflammation via the gut-liver axis. NAFLD populations exhibit a higher relative abundance of Prevotella 7 in the gut than healthy individuals ²⁴, accompanied by a corresponding elevation in LPS levels. LPS activates macrophage inflammatory responses by binding to Toll-like receptor 4 ²², exacerbating the occurrence and progression of NAFLD.

Anaerofilum is a genus of anaerobic bacteria that belongs to the Ruminococcaceae family. It has also been reported to be associated with central obesity. ²⁵ However, to date, there are very few studies related to Anaerofilum, and further research is required to investigate its functions through whole-genome sequencing or cultivation methods.

Previous studies have shown that a higher abundance of Gordonibacter is negatively correlated with metabolic syndrome ²⁶ and is associated with lower levels of gut inflammation and cell apoptosis. ²⁷ Our results, indicating the protective effect of Gordonibacter against NAFLD are consistent with these findings. The mechanism by which Gordonibacter exerts protective effects against NAFLD may involve the production of anti-inflammatory substances, such as short-chain fatty acids. ²⁸ short-chain fatty acids serve as an energy source for the intestinal epithelium and their increased production promotes the restoration of intestinal mucosal barrier function, reducing the absorption of harmful substances into the blood. ²⁹ Moreover, they mitigate hepatic lipid accumulation and inflammation via the gut-liver axis, alleviate insulin resistance, and ameliorate hepatic steatosis. ³⁰

Our study has several strengths. First, we conducted analyses at the phylum, class, order, family, and genus levels, allowing us to identify the bacteria potentially associated with NAFLD at different biological levels. We also attempted to reveal the evolutionary relationships between different gut bacteria using a phylogenetic-like plot. Second, we employed multiple methods to minimize heterogeneity and eliminate horizontal pleiotropy, thereby ensuring the robustness of our results.

However, it is important to acknowledge the limitations of this study. First, the conventional threshold of 5 × 10^− 8, which is commonly employed in Mendelian randomization studies for SNP selection, was considered overly stringent in our analysis. Therefore, we used a threshold of 1 × 10^− 5 for SNP selection in this study. Second, Actinomycetales and Actinomycetaceae employed the same IVs (Fig. 2), indicating that the GWAS summary dataset from MiBioGen may lack precision, possibly because of its reliance on 16S rRNA amplicon sequencing. Future studies should consider constructing GWAS datasets using more precise sequencing methods, such as next-generation sequencing or third-generation sequencing technologies. Third, owing to the utilization of summary statistics, we were unable to perform subgroup analyses, such as stratification based on the severity of hepatic steatosis, age, and sex.

In summary, our Mendelian randomization study robustly observed a causal relationship between gut microbiota and NAFLD. Actinomycetales, Actinomycetaceae, Actinomyces, and Prevotella 7 were found to have detrimental effects on NAFLD, with the former three belonging to the same evolutionary branch in the phylogenetic-like plot, highlighting Actinomyces as a potentially important risk factor for NAFLD. Conversely, Anaerofilum and Gordonibacter exhibit protective effects against NAFLD. These findings shed light on the role of specific gut bacteria in the pathogenesis of NAFLD and provide valuable insights into future therapeutic interventions and preventive strategies.

Data Availability Statement

The data used in this study were obtained from the MiBioGen Consortium (https://mibiogen.gcc.rug.nl/) and FinnGen (https://www.finngen.fi).

Author Contributions

Liwei Li designed the study, analyzed the data, and drafted the manuscript. Fuqing Cai and Guang Xiong have proofread the manuscript. Jiamin Qin proofread the figures. Jie-an Huang and Yan Geng are the co-corresponding authors. All authors have approved the publication of the manuscript.

Acknowledgement

We greatly appreciate the researchers of the MiBioGen and FinnGen projects for publicly sharing the GWAS data.

Funding

This study was supported in part by grants from the National Natural Science Foundation of China (grant no. 81760516), National Natural Science Foundation of China (grant no. 81741133), Natural Science Foundation of Guangxi (grant no. 2019GXNSFAA185030), Natural Science Foundation of Guangxi Key Program (grant no. 2017GXNSFDA198051), and Natural Science Foundation of Guangxi General Program (grant no. 2016GXNSFAA80313).

Conflict of interest statement: The authors declare no conflict of interest.

Sherif ZA, et al. Global Epidemiology of Nonalcoholic Fatty Liver Disease and Perspectives on US Minority Populations. Dig Dis Sci. 2016;61:1214–25. 10.1007/s10620-016-4143-0.
Byrne CD, Targher G. NAFLD: a multisystem disease. J Hepatol. 2015;62:47–64. 10.1016/j.jhep.2014.12.012.
Stefan N, Häring HU, Cusi K. Non-alcoholic fatty liver disease: causes, diagnosis, cardiometabolic consequences, and treatment strategies. Lancet Diabets Endocrionol. 2019;7:313–24. 10.1016/s2213-8587(18)30154-2.
Arab JP, Arrese M, Trauner M. Recent Insights into the Pathogenesis of Nonalcoholic Fatty Liver Disease. Annu Rev Pathol. 2018;13:321–50. 10.1146/annurev-pathol-020117-043617.
Wang W, et al. Combination of Probiotics and Salvia miltiorrhiza Polysaccharide Alleviates Hepatic Steatosis via Gut Microbiota Modulation and Insulin Resistance Improvement in High Fat-Induced NAFLD Mice. Diabetes & metabolism journal. 2020;44:336–48. 10.4093/dmj.2019.0042.
Del Chierico F, et al. Gut microbiota profiling of pediatric nonalcoholic fatty liver disease and obese patients unveiled by an integrated meta-omics-based approach. Hepatology (Baltimore MD). 2017;65:451–64. 10.1002/hep.28572.
Burgess S, et al. Guidelines for performing Mendelian randomization investigations. Wellcome open research. 2019;4. 10.12688/wellcomeopenres.15555.2.
Kurki MI, et al. FinnGen provides genetic insights from a well-phenotyped isolated population. Nature. 2023;613:508–18. 10.1038/s41586-022-05473-8.
Shi Q, Wang Q, Wang Z, Lu J, Wang R. Systemic inflammatory regulators and proliferative diabetic retinopathy: A bidirectional Mendelian randomization study. Front Immunol. 2023;14. 10.3389/fimmu.2023.1088778.
Li P, et al. Association between gut microbiota and preeclampsia-eclampsia: a two-sample Mendelian randomization study. BMC Med. 2022;20:443. 10.1186/s12916-022-02657-x.
Hartwig FP, Davey Smith G, Bowden J. Robust inference in summary data Mendelian randomization via the zero modal pleiotropy assumption. Int J Epidemiol. 2017;46:1985–98. 10.1093/ije/dyx102.
Verbanck M, Chen CY, Neale B, Do R. Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases. Nat Genet. 2018;50:693–8. 10.1038/s41588-018-0099-7.
Boulund U, et al. Gut microbiome associations with host genotype vary across ethnicities and potentially influence cardiometabolic traits. Cell Host Microbe. 2022;30:1464–1480e1466. 10.1016/j.chom.2022.08.013.
Lee G, et al. Distinct signatures of gut microbiome and metabolites associated with significant fibrosis in non-obese NAFLD. Nat Commun. 2020;11:4982. 10.1038/s41467-020-18754-5.
Loomba R, Friedman SL, Shulman GI. Mechanisms and disease consequences of nonalcoholic fatty liver disease. Cell. 2021;184:2537–64. 10.1016/j.cell.2021.04.015.
Pedersen HK, et al. Human gut microbes impact host serum metabolome and insulin sensitivity. Nature. 2016;535:376–81. 10.1038/nature18646.
Yu D, et al. The adverse metabolic effects of branched-chain amino acids are mediated by isoleucine and valine. Cell Metabol. 2021;33:905–922e906. 10.1016/j.cmet.2021.03.025.
Boursier J, et al. The severity of nonalcoholic fatty liver disease is associated with gut dysbiosis and shift in the metabolic function of the gut microbiota. Hepatology (Baltimore MD). 2016;63:764–75. 10.1002/hep.28356.
Hoyles L, et al. Molecular phenomics and metagenomics of hepatic steatosis in non-diabetic obese women. Nat Med. 2018;24:1070–80. 10.1038/s41591-018-0061-3.
Nemet I, et al. Atlas of gut microbe-derived products from aromatic amino acids and risk of cardiovascular morbidity and mortality. Eur Heart J. 2023. 10.1093/eurheartj/ehad333.
Testerman T, Li Z, Galuppo B, Graf J, Santoro N. Insights from shotgun metagenomics into bacterial species and metabolic pathways associated with NAFLD in obese youth. Hepatol Commun. 2022;6:1962–74. 10.1002/hep4.1944.
Choi EY, Lee JE, Lee AR, Choi IS, Kim SJ. Nitrooleic acid inhibits macrophage activation induced by lipopolysaccharide from Prevotella intermedia. Nutrition research (New York, N.Y.) 106, 35–46, doi:10.1016/j.nutres.2022.07.009 (2022).
Grosheva I, et al. High-Throughput Screen Identifies Host and Microbiota Regulators of Intestinal Barrier Function. Gastroenterology. 2020;159:1807–23. 10.1053/j.gastro.2020.07.003.
Schwimmer JB, et al. Microbiome Signatures Associated With Steatohepatitis and Moderate to Severe Fibrosis in Children With Nonalcoholic Fatty Liver Disease. Gastroenterology. 2019;157:1109–22. 10.1053/j.gastro.2019.06.028.
Koo SH, et al. A pilot study to examine the association between human gut microbiota and the host's central obesity. JGH open: an open access journal of gastroenterology and hepatology. 2019;3:480–7. 10.1002/jgh3.12184.
Selma MV, et al. The human gut microbial ecology associated with overweight and obesity determines ellagic acid metabolism. Food Funct. 2016;7:1769–74. 10.1039/c5fo01100k.
Hu L, et al. Nitrate ameliorates dextran sodium sulfate-induced colitis by regulating the homeostasis of the intestinal microbiota. Free Radic Biol Med. 2020;152:609–21. 10.1016/j.freeradbiomed.2019.12.002.
Thirion F, et al. The gut microbiota in multiple sclerosis varies with disease activity. Genome Med. 2023;15:1. 10.1186/s13073-022-01148-1.
Ye J et al. Butyrate Protects Mice Against Methionine-Choline-Deficient Diet-Induced Non-alcoholic Steatohepatitis by Improving Gut Barrier Function, Attenuating Inflammation and Reducing Endotoxin Levels. Frontiers in microbiology 9, 1967, doi:10.3389/fmicb.2018.01967 (2018).
Zhang S, et al. Dietary fiber-derived short-chain fatty acids: A potential therapeutic target to alleviate obesity-related nonalcoholic fatty liver disease. Obes reviews: official J Int Association Study Obes. 2021;22:e13316. 10.1111/obr.13316.

graphicalabstractHepatolInt.tif
Graphical Abstract
SupplementaryFigureS1.tif
Supplementary Figure S1Forest plot of association between gut microbiota and Non-Alcoholic Fatty Liver Disease at phylum level. Abbreviations: CI, confidence interval; MR, Mendelian randomization; MR-PRESSO, Mendelian randomization pleiotropy residual sum and outlier; ML, Maximum likelihood; nSNP, number of single nucleotide polymorphism; OR, Odds Ratio.
SupplementaryFigureS2.tif
Supplementary Figure S2Forest plot of association between gut microbiota and Non-Alcoholic Fatty Liver Disease at class level. Abbreviations: CI, confidence interval; MR, Mendelian randomization; MR-PRESSO, Mendelian randomization pleiotropy residual sum and outlier; ML, Maximum likelihood; nSNP, number of single nucleotide polymorphism; OR, Odds Ratio.
SupplementaryFigureS3.tif
Supplementary Figure S3Forest plot of association between gut microbiota and Non-Alcoholic Fatty Liver Disease at order level. Abbreviations: CI, confidence interval; MR, Mendelian randomization; MR-PRESSO, Mendelian randomization pleiotropy residual sum and outlier; ML, Maximum likelihood; nSNP, number of single nucleotide polymorphism; OR, Odds Ratio.
SupplementaryFigureS4.tif
Supplementary Figure S4Forest plot of association between gut microbiota and Non-Alcoholic Fatty Liver Disease at family level. Abbreviations: CI, confidence interval; MR, Mendelian randomization; MR-PRESSO, Mendelian randomization pleiotropy residual sum and outlier; ML, Maximum likelihood; nSNP, number of single nucleotide polymorphism; OR, Odds Ratio.
SupplementaryFigureS5.tif
Supplementary Figure S5Forest plot of association between gut microbiota and Non-Alcoholic Fatty Liver Disease at genus level. Abbreviations: CI, confidence interval; MR, Mendelian randomization; MR-PRESSO, Mendelian randomization pleiotropy residual sum and outlier; ML, Maximum likelihood; nSNP, number of single nucleotide polymorphism; OR, Odds Ratio.
SupplementaryTableS1.xls
Supplementary Table S1The Harmonise Data of Association Analysis of Gut Microbiota Features and Non-Alcoholic Fatty Liver Disease Using Mendelian Randomization
SupplementaryTableS2.xls
Supplementary Table S2The Methods and Effect Estimates of Association Analysis of Gut Microbiota Features and Non-Alcoholic Fatty Liver Disease Using Mendelian Randomization
SupplementaryTableS3.xls
Supplementary Table S3The Mendelian Randomization Pleiotropy Residual Sum and Outlier Results of Association Analysis of Gut Microbiota Features and Non-Alcoholic Fatty Liver Disease Using Mendelian Randomization
SupplementaryTableS4.xls
Supplementary Table S4The Steiger Test Results of Association Analysis of Gut Microbiota Features and Non-Alcoholic Fatty Liver Disease Using Mendelian Randomization

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The Causal Relationship between Gut Microbiota and Non-alcoholic Fatty Liver Disease: A Mendelian Randomization Study

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Abstract

Figures

Introduction

Materials and Methods

Data Source

Instrumental variable selection

Statistical analysis

Results

NAFLD-Associated Gut Microbiota Feature Selection

Sensitivity Analysis Reinforces the Robustness of Causal Relationship Between Gut Microbiota and NAFLD

Causal Effects of the Gut Microbiota on NAFLD

Relationships Among Gut Microbiota Revealed by Phylogenetic-like Plot

Discussion

Declarations

References

Supplementary Files

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