Causal relationship in gut microbiota and upper urinary urolithiasis using Mendelian randomization

The influence and relationship of dysbiosis in the urinary microbiota on patients with urolithiasis

Urolithiasis is a disease with high prevalence and recurrence rate. There are various risk factors impacting on stone formation including intestinal micorbiota. This study aims to investigate the relationship between urine microbiota with urolithiasis. We collected mid-stream voided urine samples from urolithiasis patients and control participants and stored them in a freezer at - 80 °C. All enrolled participants were requested to provide information about their clinical characteristics. The procedure included the extraction of the genomic DNA from the urine samples; the amplification by polymerase chain reaction (PCR); PCR product quantification, mixing, and purification; DNA library preparation; and sequencing was performed with quality control (QC) measures. Alpha diversity was indicative of the species complexity within individual urine samples, and beta diversity analysis was used to evaluate the differences among the samples in terms of species complexity. We enrolled 28 urolithiasis patients and 59 control participants who reported no recent antibiotic usage. In the beta diversity analysis, there was a significant difference between the microbiota in the samples of the urolithiasis and control groups according to ANOSIM statistical analysis. (P = 0.004). On comparing the groups, it showed Alcaligenes, Bacteroides, Blautia, Ruminococcaceae_UCG, Cutibacterium, Alistipes, Lachnoclostridium present more significant in control group than urolithiasis patients. In conclusion, our current study shows that dysbiosis of urine microbiota may be related to the development of urolithiasis. Further research targeting specific microbes to identify their role in the development of diseases is necessary and might provide novel diagnostic biomarkers and therapeutic options.

Lifestyle Factors and the Microbiome in Urolithiasis: A Narrative Review

Urolithiasis represents one of the most common urologic diseases, and its incidence demonstrates, globally, an increasing trend. The application of preventive measures is an established strategy to reduce urolithiasis-related morbidity, and it is based mostly on the adaptation of lifestyle factors and pharmacotherapy. Furthermore, other research areas demonstrate promising results, such as the research on the microbiome. In the current review, we searched for the latest data on lifestyle-based prevention and microbiome alterations in urolithiasis patients. The majority of the proposed lifestyle measures are already included in the urological guidelines, while additional factors, such as vitamin D supplementation, seem to have a putative positive effect. From the microbiome studies, several microbial composition patterns and metabolic pathways demonstrated an inhibiting or promoting role in lithogenesis. Up to the present, stone prevention has not shown satisfying results, which suggests that lifestyle measures are not adequate. Moreover, microbiome studies are prone to bias, since microbes are strongly affected by numerous clinical factors, while the analysis procedures are not standardized yet. Analysis standardization and data pooling from extensive registration of clinical and microbiome data are essential steps in order to improve the existing prevention strategy with targeted microbiome manipulations.

Unraveling the association of bacteria and urinary stones in patients with urolithiasis: an update review article

Urinary stone disease (USD) is a prevalent urological condition, ranking as one of the most common urinary tract disorders globally. Various risk factors influence the formation of kidney stones, and recent research indicates a rising prevalence of urolithiasis worldwide, particularly in developing countries. While the morbidity associated with urinary stones has decreased in recent years, long-term complications such as stone recurrence, kidney failure, and uremia continue to burden patients. Understanding the etiologies of urolithiasis, including the role of bacteria, is crucial as they can contribute to stone recurrence. The incidence of urinary tract infection (UTI) stones can be attributed to specific infectious risk factors, socio-demographic factors, and comorbid metabolic disorders. This review article explores the emerging evidence suggesting the involvement of bacteria in USD. It discusses the potential role of microorganisms in non-infection stones and highlights the association between UTIs and urolithiasis. Furthermore, it surveys the relationship between kidney stones and recurrent UTIs and the formation of bacterial biofilms in UTIs. Considering various risk factors, including biochemical stone analysis and the presence of bacteria, is essential for treating patients with infectious stones optimally. This review aims to provide an updated understanding of the association between bacteria and urinary stones in patients with urolithiasis, shedding light on the pathophysiology of urinary stone formation, urinary stone characteristics, and the urinary microbiome in urinary stones.

The causal relationship between gut microbiota and biliary tract cancer: comprehensive bidirectional Mendelian randomization analysis

Background

Growing evidence has shown that gut microbiome composition is associated with Biliary tract cancer (BTC), but the causality remains unknown. This study aimed to explore the causal relationship between gut microbiota and BTC, conduct an appraisal of the gut microbiome's utility in facilitating the early diagnosis of BTC.

Methods

We acquired the summary data for Genome-wide Association Studies (GWAS) pertaining to BTC (418 cases and 159,201 controls) from the Biobank Japan (BBJ) database. Additionally, the GWAS summary data relevant to gut microbiota (N = 18,340) were sourced from the MiBioGen consortium. The primary methodology employed for the analysis consisted of Inverse Variance Weighting (IVW). Evaluations for sensitivity were carried out through the utilization of multiple statistical techniques, encompassing Cochrane's Q test, the MR-Egger intercept evaluation, the global test of MR-PRESSO, and a leave-one-out methodological analysis. Ultimately, a reverse Mendelian Randomization analysis was conducted to assess the potential for reciprocal causality.

Results

The outcomes derived from IVW substantiated that the presence of Family Streptococcaceae (OR = 0.44, P = 0.034), Family Veillonellaceae (OR = 0.46, P = 0.018), and Genus Dorea (OR = 0.29, P = 0.041) exerted a protective influence against BTC. Conversely, Class Lentisphaeria (OR = 2.21, P = 0.017), Genus Lachnospiraceae FCS020 Group (OR = 2.30, P = 0.013), and Order Victivallales (OR = 2.21, P = 0.017) were associated with an adverse impact. To assess any reverse causal effect, we used BTC as the exposure and the gut microbiota as the outcome, and this analysis revealed associations between BTC and five different types of gut microbiota. The sensitivity analysis disclosed an absence of empirical indicators for either heterogeneity or pleiotropy.

Conclusion

This investigation represents the inaugural identification of indicative data supporting either beneficial or detrimental causal relationships between gut microbiota and the risk of BTC, as determined through the utilization of MR methodologies. These outcomes could hold significance for the formulation of individualized therapeutic strategies aimed at BTC prevention and survival enhancement.

Genetically predicted causality between gut microbiota, blood metabolites, and intracerebral hemorrhage: a bidirectional Mendelian randomization study

Background

Recent research linked changes in the gut microbiota and serum metabolite concentrations to intracerebral hemorrhage (ICH). However, the potential causal relationship remained unclear. Therefore, the current study aims to estimate the effects of genetically predicted causality between gut microbiota, serum metabolites, and ICH.

Methods

Summary data from genome-wide association studies (GWAS) of gut microbiota, serum metabolites, and ICH were obtained separately. Gut microbiota GWAS (N = 18,340) were acquired from the MiBioGen study, serum metabolites GWAS (N = 7,824) from the TwinsUK and KORA studies, and GWAS summary-level data for ICH from the FinnGen R9 (ICH, 3,749 cases; 339,914 controls). A two-sample Mendelian randomization (MR) study was conducted to explore the causal effects between gut microbiota, serum metabolites, and ICH. The random-effects inverse variance-weighted (IVW) MR analyses were performed as the primary results, together with a series of sensitivity analyses to assess the robustness of the results. Besides, a reverse MR was conducted to evaluate the possibility of reverse causation. To validate the relevant findings, we further selected data from the UK Biobank for analysis.

Results

MR analysis results revealed a nominal association (p < 0.05) between 17 gut microbial taxa, 31 serum metabolites, and ICH. Among gut microbiota, the higher level of genus Eubacterium xylanophilum (odds ratio (OR): 1.327, 95% confidence interval (CI):1.154-1.526; Bonferroni-corrected p = 7.28 × 10-5) retained a strong causal relationship with a higher risk of ICH after the Bonferroni corrected test. Concurrently, the genus Senegalimassilia (OR: 0.843, 95% CI: 0.778-0.915; Bonferroni-corrected p = 4.10 × 10-5) was associated with lower ICH risk. Moreover, after Bonferroni correction, only two serum metabolites remained out of the initial 31 serum metabolites. One of the serum metabolites, Isovalerate (OR: 7.130, 95% CI: 2.648-19.199; Bonferroni-corrected p = 1.01 × 10-4) showed a very strong causal relationship with a higher risk of ICH, whereas the other metabolite was unidentified and excluded from further analysis. Various sensitivity analyses yielded similar results, with no heterogeneity or directional pleiotropy observed.

Conclusion

This two-sample MR study revealed the significant influence of gut microbiota and serum metabolites on the risk of ICH. The specific bacterial taxa and metabolites engaged in ICH development were identified. Further research is required in the future to delve deeper into the mechanisms behind these findings.

Genetic evidence on the causality between gut microbiota and various asthma phenotypes: a two-sample Mendelian randomization study

Introduction

Asthma is a multifarious disease that manifests in various phenotypes. Among the various factors that contribute to the development of asthma, the gut microbiota has recently emerged as a compelling area of investigation. This study aims to investigate the causal relationships between gut microbiota and distinct asthma phenotypes.

Methods

The genome-wide association study (GWAS) summary statistics for 211 gut microbial taxa were used as study exposure. Five traits pertaining to various asthma phenotypes (asthma, allergic asthma, childhood asthma, suggestive for eosinophilic asthma and obesity-related asthma) were included as study outcome. We conducted Mendelian randomization (MR) analysis and sensitivity analysis for each bacterial taxa and asthma phenotypes.

Result

We discovered a total of 58 associations that exhibited evidence of causality. Out of these, 4 associations remained significant even after applying multiple correction. An increased risk of asthma was causally associated with higher abundance of genus Holdemanella (OR = 1.11; CI: 1.05-1.17; p = 0.027), genus Oxalobacter (OR = 1.09; CI: 1.04-1.15; p = 0.025) and genus Butyricimonas (OR = 1.14; CI: 1.06-1.22; p = 0.027). Order NB1n was causally linked with an increased risk of obesity-related asthma (OR = 1.17; CI: 1.07-1.29; p = 0.015). There was limited overlap among the taxa that exhibited potential causal relationships with distinct asthma phenotypes.

Conclusion

Our research has provided genetic evidence that establishes multiple causal relationships between the gut microbiota and distinct asthma phenotypes, supporting the role of the gut microbiota in various asthma phenotypes. It is possible that different taxa play a role in the development of distinct asthma phenotypes. The causal relationships identified in this study require further investigation.

Impact of gut microbiota on nonalcoholic fatty liver disease: insights from a leave-one-out cross-validation study

Introduction

Enteric dysbacteriosis is strongly associated with nonalcoholic fatty liver disease (NAFLD). However, the underlying causal relationship remains unknown. Thus, the present study aimed to investigate the relationship between gut microbiota and NAFLD using Mendelian randomization (MR) and analyze the target genes potentially regulated by specific microbiota.

Methods

Bidirectional two-sample MR analysis was performed using inverse variance weighted (IVW) supplemented by MR-Egger, weighted median, simple mode, and weighted mode methods. Data were pooled from gut microbiota and NAFLD association studies. The least absolute shrinkage, selection operator regression, and the Support Vector Machine algorithm were used to identify genes regulated by these intestinal flora in NAFLD. The liver expression of these genes was verified in methionine choline-deficient (MCD) diet-fed mice.

Results

IVW results confirmed a causal relationship between eight specific gut microbes and NAFLD. Notably, the order Actinomycetales, NB1n, the family Actinomycetaceae, Oxalobacteraceae and the genus Ruminococcaceae UCG005 were positively correlated, whereas Lactobacillaceae, the Christensenellaceae R7 group, and Intestinibacter were negatively correlated with NAFLD onset. In NAFLD, these eight bacteria regulated four genes: colony-stimulating factor 2 receptor β, fucosyltransferase 2, 17-beta-hydroxysteroid dehydrogenase 14, and microtubule affinity regulatory kinase 3 (MAPK3). All genes, except MARK3, were differentially expressed in the liver tissues of MCD diet-fed mice.

Discussion

The abundance of eight gut microbiota species and NAFLD progression displayed a causal relationship based on the expression of the four target genes. Our findings contributed to the advancement of intestinal microecology-based diagnostic technologies and targeted therapies for NAFLD.