Exploring the Causal Relationship Between Gut Microbiota and Inflammatory Bowel Disease Using Two-Sample Mendelian Randomization, and Bayesian Weighted Validation Volume 59- Issue 2

Jieqiong Qi^1,2, Yangfan Xu^1,2, Jiayao Liu^2,3, Wujie Zhao², Bin Wang² and Yitao Jia^2*

• ¹Hebei North University, China
• ²Department of Oncology, Hebei General Hospital, China
• ³Hebei Medical University, China

Received: September 26, 2024; Published: October 23, 2024

*Corresponding author: Yitao Jia, Professor, Department of Oncology, Hebei General Hospital, No. 348 Heping West Road, Shijiazhuang 050051, Hebei Province, China This work was supported by The Specialist Capacity Building and Leader Development Program Funded from Hebei Government.

DOI: 10.26717/BJSTR.2024.59.009262

Abstract PDF

ABSTRACT

Background: A growing body of research suggests a link between the gut microbiota and inflammatory bowel disease (IBD）, but the causal relationship between specific flora and inflammatory bowel disease is unclear. The aim of this study was to investigate the causal relationship between gut microbiota genera and inflammatory bowel disease and its two phenotypes ulcerative colitis and Crohn's disease.

Methods: To elucidate the causal relationship between gut microbiota and inflammatory bowel disease, we obtained summary statistics of gut microbiota and IBD, Ulcerative colitis (UC) and Crohn's disease (CD) from published genome-wide association studies (GWAS). The inverse-variance-weighted (IVW) method was used as the main analytical method for Mendelian randomization (MR) analysis, and we used MR-Egger, Weighted median methods, Simple mode and Weighted mode methods as a supplement to the IVW method. During sensitivity analysis, we used MR-Egger regression intercept method to test for the presence of pleiotropy. Cochran's Q test was used to test for heterogeneity across SNPs. Finally, we also validated the results using Bayesian weighting.

Results: In the absence of heterogeneity and horizontal pleiotropy, the IVW method revealed that Eubacteriumruminantiumgroup, LachnospiraceaeFCS020group, Oxalobacter can increase the risk of IBD, Ruminococcus2, Clostridiumsensustricto1, Lactobacillus could decrease the risk of IBD (all P <0.05). Ruminococcus2, Clostridiumsensustricto1, Lactobacillus could decrease the risk of IBD. RuminococcaceaeUCG010, Oxalobacter can increase the risk of UC, Eggerthella, RuminococcaceaeUCG009, Hungatella, LachnospiraceaeUCG001, LachnospiraceaeNK4A136group, Dialister can decrease the risk of UC. Coprococcus2, Eubacteriumruminantiumgroup can increase the risk of CD, Clostridiumsensustricto1, Catenibacterium, Eubacteriumventriosumgroup, Lactobacillus can decrease the risk of CD.

Conclusion: This study revealed the causal relationship between gut microbiota genus and IBD, UC, and CD, and provided new ideas for the treatment and prevention of IBD.

Keywords: Gut Microbiota; Inflammatory Bowel Disease; Ulcerative Colitis; Crohn’s Disease; Mendelian Randomization

Abbreviations: IBD: Inflammatory Bowel Disease; UC: Ulcerative Colitis; CD: Crohn’s Disease. LD: Linkage Disequilibrium, which used to measure the correlations between SNPs; IVW: Inverse Variance-Weighted; SNP: Single Nucleotide Polymorphism, as instrumental variables for the exposures and outcomes. BWMR: Bayesian Weighted Mendelian Randomization; MR: Mendelian Randomization; SE: Standard Error; OR: Odds Ratios; CI: Confidence Interval; IVW: Inverse Variance Weighting; UC: Ulcerative Colitis

Introduction

Inflammatory bowel disease (IBD) is a chronic relapsing inflammatory disease of the gastrointestinal tract, the main types of which include Ulcerative colitis (UC) and Crohn's disease (CD) [1,2]. It has been reported that nearly 3.9 million females and nearly 3 million males are currently suffering from IBD globally and the number of cases is rising. The incidence of IBD is rapidly increasing, especially in newly industrialized countries in South America, Eastern Europe, Asia and Africa [3]. Because it is a lifelong disease, it usually develops when the patient is young and causes great physical and mental suffering. Although the etiology and pathogenesis of IBD are unknown, current research suggests that the pathogenesis of IBD is related to environmental factors, genetic factors, gut microbiota, environmental factors, and immune system dysregulation [4].

A growing number of data now suggests that alterations in the gut microbiota are associated with inflammatory bowel disease, including changes in the relative abundance of the flora and a decrease in the diversity of the flora [5-7]. Compared to healthy individuals, alterations in the gut microbiota observed in patients with IBD include reductions in Bacteroides, Firmicutes, Clostridia, Ruminococcaceae, Bifidobacterium, Lactobacillus, and Faecalibacterium prausnitzii [8-11]. However, the conclusion of the relationship between specific gut microbiota and IBD is not clear. Controversial views about the changes in the gut microbiota of IBD patients have been obtained in some gut microbiota, including Bifidobacterium, Clostridiales, Clostridium difficile, Campylobacter, Helicobacter and Faecalibacterium prausnitzii [12]. Studies have found abundance of Haemophilus and Desulfovibrio (affiliated with Proteobacteria) decreased in patients with UC [13]. Reduced abundance of Bacteroidetes in patients with CD [14].

The relationship between gut microbiota associated with IBD, CD and UC is unclear. Most of the previous studies are observational or experimental, which will have some bias, this article uses MR to explore the causal relationship between gut microbiota genera and IBD, UC or CD. MR is a method that uses genetic variants closely associated with exposure as instrumental variables (IVs) from which causal relationships between exposure factors and outcomes can be inferred [15]. Since genes are randomly assigned at birth and parental alleles are randomly assigned to offspring, Mendelian randomization has the natural advantage of being independent of traditional confounders and of satisfying temporal rationality [16]. Mendelian randomization, based on large-scale genome-wide association studies (GWAS), is effective in reducing bias and is a higher level of evidence for RCT studies.

Methods

The overall study design of the article is shown in Figure 1. We used a two-sample Mendelian randomization method to investigate the causal relationship between the gut microbiota and IBD, as well as the relationship between the gut microbiota and UC and CD respectively. To ensure the reliability of the results, we tried to meet the three main assumptions of MR analysis in our study:

1) The genetic instrumental variable must show a strong correlation with the exposure factor (gut microbiota);

2) The genetic instrumental variable does not directly affect the outcome, and the instrumental variable can only be related to the outcome through the exposure factor; and

3) The genetic instrumental variable does not correlate with any potential confounders [17]. Finally, we also validate the results using Bayesian weighting.

Data Sources for Exposure Data

SNPs associated with the composition of the human gut microbiome selected from the MiBioGen Consortium GWAS dataset (Link to data: https: //mibiogen.gcc.rug.nl). The MiBioGen consortium coordinated 16 S rRNA gene sequencing profiles and genotyping data for 18,340 participants from 24 cohorts in the United States, Canada, Israel, Korea, Germany, Denmark, the Netherlands, Belgium, Sweden, Finland, and the United Kingdom in this study. This study conducted a large-scale, multi-ethnic, genome-wide meta-analysis of associations between human autosomal genetic variants and the gut microbiome. We analyzed gut microbiota taxa at five levels (phylum, class, order, family, genus). Firstly, to ensure that SNPs were strongly correlated with gut microbiota, we used P < 1 × 10 ^{- 5} as a threshold for selecting SNPs. secondly, to minimize the bias caused by allelic associations, the clumping process was set with R² < 0.001, and clumping distance = 10,000 kb to remove the chaining imbalance. Finally, we usually considered F-statistics > 10 as strong instrumental variables (F-statistics > 10 were set as the threshold of strong IVs). F-statistics were calculated using the following formula: F = R² (n-k-1)/k(1-R²). Where R²denotes the variance explained by IVs (each gut microbiome) and n denotes the sample size. R2 was estimated from the minor allele frequency (MAF) and the b-value using the formula: R² =2×MAF ×（1−MAF）× b² [18].

Data Sources for Outcome Data

All datasets in the article of the outcome are freely accessible from the IEU Open GWAS program (https:// gwas. mrcieu. ac. uk/). All case and control groups were mixed populations. GWAS summary data for IBD consisted of 25,042 cases and 34,915 controls with a total of 9,619,016 SNPs. GWAS summary data for UC consisted of 12,366 cases and 33,609 controls with a total of 9,474,559 SNPs. GWAS summary data for CD included 12,194 cases and 28,072 control cases, totaling 9,457,998 SNPs. Because we used data from published studies and publicly available database statistics, there were no ethical concerns. All analyses and associated images in the article were performed in R (version 4.3.2) using the "TwoSample MR (0.6.1)" software package.

MR Analysis

In this paper, we used inverse variance weighted analysis（ IVW） as the main method for MR analysis, IVW is the wald ratio of multiple SNPs by using meta-analysis to derive an overall estimate of effect [19]. The IVW method is plausible in the absence of horizontal pleiotropy. We used MR-Egger, weighted median methods, simple mode and weighted mode methods as a supplement to the IVW method. A P<0.05 would indicate a potential causal relationship between gut microbiota and outcome inflammatory bowel disease.

Sensitivity Analysis

To assess the reliability of the results of the MR analyses and to detect potential bias and the effect of the instrumental variables on the outcome, we performed sensitivity analyses. In order to test whether pleiotropy existed and to ensure that the instrumental variables could only affect the outcome through the gut microbiota and that the instrumental variables did not directly affect the outcome, we used the MR-Egger regression intercept method to test whether pleiotropy existed [20]. A P<0.05 indicates the presence of potential horizontal pleiotropy, and a P>0.05 indicates reliable results. To test for heterogeneity across SNPs, we performed Cochran's Q test21. Heterogeneity was considered to be present if a significant difference (P<0.05) was observed. Then random effect IVW model was used, otherwise fixed effect IVW model was used [21]. In addition, we performed Leave out analysis and Funnel plot for analytical validation in order to exclude the driving or bias caused by single SNPs.

Bayesian Weighted Verification

There are several possible problems with using two-sample Mendelian randomization for the analysis: First, in the presence of a polygenic structure, there are many weak SNPs exposure effects, i.e., SNPs are not strongly correlated with exposure, and the uncertainty of weak SNP effects needs to be taken into account. Second, it was observed that many SNPs can directly affect outcome. That is, there is "pleiotropy". This can lead to false-positive results. Third, MR based on generalized genetics may involve many potential risks, such as selection bias for SNP exposure effects and other biases due to overlapping samples [22]. These factors can lead to inaccurate results, and to avoid these problems, we validated them with Bayesian weighted Mendelian randomization (BWMR) causal inference. To improve the computational stability and efficiency of BWMR causal inference, they developed a variational expectation maximization (VEM) algorithm that is statistically efficient and computationally stable. Therefore, we use this approach to test the results of the IVW method [22].

Result

In order to ensure the correctness and reliability of the conclusions, we ensured the following in the process of selecting SNPs. firstly, we ensured the strong correlation between the genetic instrumental variables and the exposed gut microbiota. secondly, we removed the chain disequilibrium (Linkage disequilibrium refers to the fact that genetic variants with similar genomic locations are more likely to be co-inherited, which can result in alleles belonging to two or more genetic loci appearing on a chromosome at the same time more often than at random). Lastly, we selected the SNPs with an F -statistics > 10. The characteristics of the selected SNPs for each gut microbiota are presented. 1531 SNPs were finally selected out of 119 genera of bacterial groups that were strongly correlated with exposure and were not affected by weak instrumental. The results of the correlations between the 119 colony genera and the risk of IBD (including UC and CD) are presented. We finally obtained results that were associated with the risk of IBD, UC, and CD. and the results remained stable in the sensitivity analyses, as shown in Table 1.

Note: The Cochran’Q test was used to assess the heterogeneity, MR-Egger regression to test for evidence of pleiotropy.

IBD

In the MR analysis, based on the results of IVW, six genera of gut microbiota were observed to be correlated with the risk of IBD. three bacterial genera were risk factors for IBD and three bacterial genera were protective factors for IBD (Table 1). This result remained stable after heterogeneity analysis and horizontal pleiotropy tests (Table 1). BWMR also verified the reliability of this result (Figure 2). We observed a significant difference between the results of the Eubacteriumruminantiumgroup [odds ratio (OR)：1.087, 95% confidence interval (CI): 1.006-1.174, P=0.035], the LachnospiraceaeFCS020group (OR: 1.172, 95%CI: 1.035-1.326, P=0.012), and Oxalobacter (OR: 1.088, 95%CI: 1.010-1.171, P= 0.025) may be risk factors for IBD; However Ruminococcus2 (OR: 0.875, 95%CI: 0.775-0.989, P=0.032), Clostridiumsensustricto1 (OR: 0.835, 95%CI: 0.713-0.977, P=0.025), Lactobacillus (OR: 0.869, 95%CI: 0.772-0.979, P= 0.021) may be protective factors for IBD.

In sensitivity analyses, the results are shown in Table 1, where the use of MR-Egger regression intercept did not reveal the presence of SNP pleiotropy (MR-Egger regression intercept P=0.916 for Eubacteriumruminantiumgroup; MR-Egger regression intercept P=0.354 for LachnospiraceaeFCS020group; MR-Egger regression intercept P = 0.285 for Oxalobacter; MR-Egger regression intercept P = 0.372 for Ruminococcus2; MR-Egger regression intercept P = 0.389 for Clostridiumsensustricto1; MR-Egger regression intercept P=0.677 for Lactobacillus).Cochran’ Q test showed that these SNPs were not heterogeneous and the results were more stable. In addition, we also performed leave one out sensitivity analysis on the IVW results, and the results are shown in Figure 3. After excluding individual SNPs one by one, the results are still consistent, indicating that no single SNP has an excessive effect on the total estimate. Finally, we validated this result with BWMR, and the results are shown in Figure 2 (OR for Eubacteriumruminantiumgroup:1.091, 95%CI: 1.006-1.184, P=0.036; OR for LachnospiraceaeFCS020group:1.184, 95%CI: 1.034-1.356, P=0.015;OR for Oxalobacter: 1.091, 95%CI: 1.010-1.179, P=0.027; OR for Ruminococcus2: 0.872, 95%CI: 0.765-0.994, P=0.040;OR of Clostridiumsensustricto1: 0.821, 95%CI: 0.688-0.979, P=0.028;OR of Lactobacillus: 0.861, 95%CI: 0.765- 0.970, P=0.014).

We observed that eight genera of gut microbiota were correlated with the risk of UC, and one genus, Oxalobacter, was also correlated with IBD. Based on the results of IVW, it can be seen that RuminococcaceaeUCG010 (OR: 0.001, 95%CI: 1.169-1.807, P=0.024;) , Oxalobacter (OR: 1.152, 95%CI: 1.047-1.267, P= 0.004) may be risk factors for UC; However Eggerthella (OR: 0.872, 95%CI: 0.780-0.976, P=0.017), RuminococcaceaeUCG009 (OR: 0.857, 95%CI: 0.749-0.980, P =0.024), Hungatella (OR: 0.843, 95%CI:0.730-0.972, P=0.019), LachnospiraceaeUCG001 (OR: 0.807, 95%CI: 0.704-0.924, P=0.002), LachnospiraceaeNK4A136group (OR: 0.845, 95%CI: 0.727-0.981, P=0.027), and Dialister (OR: 0.766, 95%CI: 0.634-0.926, P=0.006) may be protective factors for UC factor. In the sensitivity analysis, the results are shown in Table 1, using the MR-Egger regression intercept method did not find the presence of multiplicity of SNPs and Cochran’ Q test showed that there was no heterogeneity of these SNPs. In addition, we also performed leave one out sensitivity analysis on the results of IVW, and the results are shown in Figure 4. After excluding individual SNPs one by one, the results are still consistent, indicating that no single SNP has an excessive effect on the total estimate. Finally, we validated this result with BWMR, and the results are shown in Figure 2, which further validates the reliability of the results.

CD

We observed that six genera were correlated with the risk of CD, and three of them, Clostridiumsensustricto1, Eubacteriumruminantium group, and Lactobacillus were also correlated with the risk of developing IBD. Based on the results of IVW, it can be seen that Coprococcus2 (OR: 1.223, 95%CI: 1.017-1.470, P=0.033), Eubacteriumruminantium group (OR: 1.116, 95%CI: 1.009-1.234, P=0.034) may be risk factors for CD; However, Clostridiumsensustricto1 (OR: 0.812, 95%CI: 0.665-0.992, P=0.041), Catenibacterium (OR: 0.877, 95%CI: 0.777-0.990, and P=0.033), Eubacteriumventriosumgroup (OR: 0.781, 95%CI: 0.627-0.973, P=0.027), Lactobacillus (OR: 0.856, 95%CI: 0.745-0.984, P=0.030) may be protective factors for CD. The conclusions obtained from sensitivity analysis and BWMR were consistent with the above (results are shown in Table 1, Figures 2 & 5). Among them, Clostridiumsensustricto1, Eubacteriumventriosumgroup, and Lactobacillus were also associated with risk for IBD.

Discussion

This study investigated the causal relationship between gut microbiota and IBD (including UC and CD) using two-sample MR. Compared to previous studies, this study has the following advantages:

i. We used a larger sample size, which makes the results more comprehensive.

ii. We performed Bayesian-weighted validation of the results obtained, which makes the results more reliable.

It was found that Eubacteriumruminantiumgroup, LachnospiraceaeFCS020group and Oxalobacter were positively associated with the risk of IBD, while Ruminococcus2 and Lactobacillus and Clostridiumsensustricto1 were negatively associated with the risk of IBD. In addition, these microorganisms show complex interrelationships with cognitive performance, psychological functioning, neurological diseases, and psychiatric behavior. In a cross-sectional study, it was shown that neurocognitive and psychomotor functions in perceptual abilities, convergent thinking and complex operant thinking were impaired in patients with IBD compared to normal subjects [23].

Previous studies have shown that patients with IBD exhibit deleterious neuropsychological effects, although the exact pathophysiologic mechanisms have not been fully elucidated [24-27]. Short-chain fatty acids (SCFA), particularly butyric acid, play an important role in gut-brain interactions. SCFA not only play a key role in energy homeostasis, colonic motility, and immune regulation [28], but also influence psychological functions, including affective and cognitive processes [29]. This suggests a potential impact of gut microbiota on mental health. Although the exact mechanisms are unknown, however, the importance of gut-brain-microbiome interactions has been emphasized, providing new insights into IBD pathophysiology and therapeutic options [24]. The present study also found that Ruminococcus2, Lactobacillus was negatively associated with the risk of IBD.Ruminococcus2 has been found to be positively associated with body weight (including waist circumference and body mass index) and serum lipids (including LDL, triglycerides, and total cholesterol) markers in a previous study, which increases the risk of obesity [24]. Interestingly, the prevalence of IBD has increased along with obesity and overweight, and about 15-40% of IBD patients are obese, which may contribute to the development of IBD [30,31].

This contradicts our study. Lactobacillus is the most important probiotic among gut microorganisms that can mediate the development of anti-inflammatory response, which may improve the symptoms of IBD [32]. Eggerthella, RuminococcaceaeUCG009, Hungatella, LachnospiraceaeUCG001, LachnospiraceaeNK4A136group, Dialister decrease the risk of UC. RuminococcaceaeUCG010, Oxalobacter are risk factors for UC. Among them, Oxalobacter is correlated with both IBD and UC. Oxalobacter increases the risk of IBD has been reported in previous studies [6]. Eggerthella and Hungatella and LachnospiraceaeUCG001 are associated with depressive symptoms and are involved in the synthesis of depression-related neurotransmitters [33]. It is well known that patients with IBD are at increased risk for anxiety and depression [34,35], so whether changes in gut microbiota led to both anxiety and depression and IBD, or whether they are sequential, needs to be further explored. LachnospiraceaeNK4A136group and Dialister have also been identified as being associated with cognitive performance. Dialister has also been shown to be associated with many neurological and psychiatric disorders [36]. The importance of gut-brain-microbiome interactions is also re-emphasized.

The importance of the gut-brain-microbiome axis was again demonstrated in the flora associated with CD. Clostridiumsensustricto1 is not only a protective factor for IBD but also for CD. Clostridiumsensustricto1 has been reported to be associated with an increased risk of Parkinson's disease (PD). catenibacterium has been associated with an increased risk of amyotrophic lateral sclerosis (ALS). Catenibacterium is associated with neurodegenerative diseases, including Alzheimer's disease (AD), Parkinson's disease (PD), and amyotrophic lateral sclerosis (ALS), via the gut-brain [37]. This finding reaffirms the inevitable gut-brain-microbe connection. The results of this study provide new ideas for the treatment and prevention of IBD. By modulating the gut microbiome, it may be possible to improve the mental health and overall health of IBD patients. This provides an important reference for future clinical interventions. Although this study provides important findings at the genus level, future studies should delve into the species level to reveal more precise mechanisms and therapeutic targets. Meanwhile, the mechanisms by which changes in gut microbiota affect mental health and neurodegenerative diseases should be further explored.

Acknowledgments

We are grateful to the participants in the MiBioGen, IEU, and other consortiums or studies and to all the researchers who worked on the data collection.

Funding and Conflict of Interest

The authors declare that no funds, grants, or other support were received during the preparation of this manuscript. The authors have no relevant financial or non-financial interests to disclose.

Data Availability Statement

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

Author Contributions

In our study, the contributions of each co-author are as follows: Jieqiong Qi: Subject facilitator, Drafting of the manuscript; Yangfan Xu: Data acquisition, Data analysis and interpretation; Jiayao Liu: Critical revision of the manuscript for important intellectual content; Wujie Zhao、Bin Wang: Study supervision; Yitao Jia (corresponding author): Study concept and design, administrative, technical support.

References

1. Bruner LP, White AM, Proksell S (2023) Inflammatory Bowel Disease. Prim Care 50(3): 411-427.
2. Khor B, Gardet A, Xavier RJ (2011) Genetics and pathogenesis of inflammatory bowel disease. Nature 474(7351): 307-317.
3. Alatab S, Sepanlou SG, Ikuta K, Eduarda Fernandes, Mohsen Naghavi, et al. (2020) The global, regional, and national burden of inflammatory bowel disease in 195 countries and territories, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet Gastroenterol Hepatol 5(1): 17-30.
4. Guan Q (2019) A Comprehensive Review and Update on the Pathogenesis of Inflammatory Bowel Disease. J Immunol Res 2019: 7247238.
5. Knights D, Lassen KG, Xavier RJ (2013) Advances in inflammatory bowel disease pathogenesis: linking host genetics and the microbiome. Gut 62(10): 1505-1510.
6. Liu B, Ye D, Yang H, Zhixing He, Yingying Mao, et al. (2022) Two Sample Mendelian Randomization Analysis Investigates Causal Associations Between Gut Microbial Genera and Inflammatory Bowel Disease, and Specificity Causal Associations in Ulcerative Colitis or Crohn’s Disease. Front Immunol 13: 921546.
7. Haneishi Y, Furuya Y, Hasegawa M, Picarelli A, Rossi M, et al. (2023) Inflammatory Bowel Diseases and Gut Microbiota. Int J Mol Sci 24(4): 3817.
8. Kostic AD, Xavier RJ, Gevers D (2014) The microbiome in inflammatory bowel disease: current status and the future ahead. Gastroenterology 146(6): 1489-1499.
9. Krause DO, Little AC, Dowd SE, Bernstein CN (2011) Complete genome sequence of adherent invasive Escherichia coli UM146 isolated from Ileal Crohn’s disease biopsy tissue. J Bacteriol 193(2): 583.
10. Kang S, Denman SE, Morrison M, Zhongtang Yu, Joel Dore, et al. (2010) Dysbiosis of fecal microbiota in Crohn’s disease patients as revealed by a custom phylogenetic microarray. Inflamm Bowel Dis 16(12): 2034-2042.
11. Morgan XC, Tickle TL, Sokol H, Curtis Huttenhower, Bruce E Sands, et al. (2012) Dysfunction of the intestinal microbiome in inflammatory bowel disease and treatment. Genome Biol 13(9): R79.
12. Li J, Butcher J, Mack D, Stintzi A (2015) Functional impacts of the intestinal microbiome in the pathogenesis of inflammatory bowel disease. Inflamm Bowel Dis 21(1): 139-153.
13. Chen L, Wang W, Zhou R, Bing Xia, Kaichun Wu, et al. (2014) Characteristics of fecal and mucosa-associated microbiota in Chinese patients with inflammatory bowel disease. Medicine (Baltimore) 93(8): e51.
14. Ma HQ, Yu TT, Zhao XJ, Zhang Y, Zhang HJ (2018) Fecal microbial dysbiosis in Chinese patients with inflammatory bowel disease. World J Gastroenterol 24(13): 1464-1477.
15. Sekula P, Del Greco M F, Pattaro C, Köttgen A (2016) Mendelian Randomization as an Approach to Assess Causality Using Observational Data. J Am Soc Nephrol JAS 27(11): 3253-3265.
16. Birney E (2022) Mendelian Randomization. Cold Spring Harb Perspect Med 12(4): a041302.
17. Davies NM, Holmes MV, Davey Smith G (2018) Reading Mendelian randomisation studies: a guide, glossary, and checklist for clinicians. BMJ 2362: k601.
18. Kamat MA, Blackshaw JA, Young R, James R Staley, Adam S Butterworth, et al. (2019) PhenoScanner V2: an expanded tool for searching human genotype-phenotype associations. Bioinforma Oxf Engl 35(22): 4851-4853.
19. Burgess S, Butterworth A, Thompson SG (2013) Mendelian randomization analysis with multiple genetic variants using summarized data. Genet Epidemiol 37(7): 658-665.
20. Bowden J, Davey Smith G, Burgess S (2015) Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. Int J Epidemiol 44(2): 512-525.
21. Greco M FD, Minelli C, Sheehan NA, Thompson JR (2015) Detecting pleiotropy in Mendelian randomisation studies with summary data and a continuous outcome. Stat Med 34(21): 2926-2940.
22. Zhao J, Ming J, Hu X, Chen G, Liu J, et al. (2020) Bayesian weighted Mendelian randomization for causal inference based on summary statistics. Bioinforma Oxf Engl 36(5): 1501-1508.
23. Tadin Hadjina I, Zivkovic PM, Matetic A, Josko Bozic, Ante Tonkic, et al. (2019) Impaired neurocognitive and psychomotor performance in patients with inflammatory bowel disease. Sci Re 9(1): 13740.
24. Gareau MG (2016) Cognitive Function and the Microbiome. Int Rev Neurobiol 131: 227-246.
25. Singh S, Singh H, Loftus EV, Pardi DS (2014) Risk of cerebrovascular accidents and ischemic heart disease in patients with inflammatory bowel disease: a systematic review and meta-analysis. Clin Gastroenterol Hepatol Off Clin Pract J Am Gastroenterol Assoc. 12(3): 382-393.e1: quiz e22.
26. Cao W, Xing M, Liang S, Shi Y, Li Z, et al. (2024) Causal relationship of gut microbiota and metabolites on cognitive performance: A mendelian randomization analysis. Neurobiol Dis 191: 106395.
27. Casella G, Tontini GE, Bassotti G, Maurizio Vecchi, Vittorio Baldini, et al. (2014) Neurological disorders and inflammatory bowel diseases. World J Gastroenterol 20(27): 8764-8782.
28. Mukherjee A, Lordan C, Ross RP, Cotter PD (2020) Gut microbes from the phylogenetically diverse genus Eubacterium and their various contributions to gut health. Gut Microbes 12(1): 1802866.
29. Silva YP, Bernardi A, Frozza RL (2020) The Role of Short-Chain Fatty Acids from Gut Microbiota in Gut-Brain Communication. Front Endocrinol 11: 25.
30. Zeng Q, Li D, He Y, Wenkui Dai, Shuaicheng Li, et al. (2019) Discrepant gut microbiota markers for the classification of obesity-related metabolic abnormalities. Sci Rep 9(1): 13424.
31. Singh S, Dulai PS, Zarrinpar A, Ramamoorthy S, Sandborn WJ (2017) Obesity in IBD: epidemiology, pathogenesis, disease course and treatment outcomes. Nat Rev Gastroenterol Hepato 14(2): 110-121.
32. Rastogi S, Singh A (2022) Gut microbiome and human health: Exploring how the probiotic genus Lactobacillus modulate immune responses. Front Pharmacol 13: 1042189.
33. Radjabzadeh D, Bosch JA, Uitterlinden AG, Anja Lok, Susanne R de Rooij, et al. (2022) Gut microbiome-wide association study of depressive symptoms. Nat Commun 13(1): 7128.
34. Walker JR, Ediger JP, Graff LA, Charles N Bernstein, Cory M McPhail, et al. (2008) The Manitoba IBD cohort study: a population-based study of the prevalence of lifetime and 12-month anxiety and mood disorders. Am J Gastroenterol 103(8): 1989-1997.
35. Panara AJ, Yarur AJ, Rieders B, S Proksell, A R Deshpande, et al. (2014) The incidence and risk factors for developing depression after being diagnosed with inflammatory bowel disease: a cohort study. Aliment Pharmacol Ther 39(8): 802-810.
36. Zang Y, Lai X, Li C, Ding D, Wang Y, et al. (2023) The Role of Gut Microbiota in Various Neurological and Psychiatric Disorders-An Evidence Mapping Based on Quantified Evidence. Mediators Inflamm 2023: 5127157.
37. Ning J, Huang SY, Chen SD, Zhang YR, Huang YY, et al. (2022) Investigating Casual Associations Among Gut Microbiota, Metabolites, and neurodegenerative diseases: A Mendelian Randomization Study. J Alzheimers Dis JAD 87(1): 211-222.