Association between gut microbiome and intracerebral hemorrhage based on genome-wide association study data. / 基于GWASæ•°æ®åˆ†æžè‚ é“èŒç¾¤ä¸Žè„‘å‡ºè¡€çš„å ³ç³».

OBJECTIVES: Intracerebral hemorrhage (ICH) has the highest mortality and disability rates among various subtypes of stroke. Previous studies have shown that the gut microbiome (GM) is closely related to the risk factors and pathological basis of ICH. This study aims to explore the causal effect of GM on ICH and the potential mechanisms. METHODS: Genome wide association study (GWAS) data on GM and ICH were obtained from Microbiome Genome and International Stroke Genetics Consortium. Based on the GWAS data, we first performed Mendelian randomization (MR) analysis to evaluate the causal association between GM and ICH. Then, a conditional false discovery rate (cFDR) method was conducted to identify the pleiotropic variants. RESULTS: MR analysis showed that Pasteurellales, Pasteurellaceae, and Haemophilus were negatively correlated with the risk of ICH, whileVerrucomicrobiae, Verrucomicrobiales, Verrucomicrobiaceae, Akkermansia, Holdemanella, and LachnospiraceaeUCG010 were positively correlated with ICH. By applying the cFDR method, 3 pleiotropic loci (rs331083, rs4315115, and rs12553325) were found to be associated with both GM and ICH. CONCLUSIONS: There is a causal association and pleiotropic variants between GM and ICH.

Gut microbiota and hypertensive disorders in pregnancy: evidence from the Mendelian randomization study.

BACKGROUND: Recent studies have shown that gut microbiota (GM) is related to hypertensive disorders in pregnancy (HDP). However, the causal relationship needs to be treated with caution due to confounding factors and reverse causation. METHODS: We obtained genetic variants from genome-wide association studies including GM (N = 18,340) in MiBioGen Consortium as well as HDP (7,686 cases/115,893 controls) and specific subtypes in FinnGen Consortium. Then, Inverse variance weighted, maximum likelihood, weighted median, MR-Egger, and MR.RAPS methods were applied to examine the causal association. Reverse Mendelian randomization (RMR) and multivariable MR were performed to confirm the causal direction and adjust the potential confounders, respectively. Furthermore, sensitivity analyses including Cochran's Q statistics, MR-Egger intercept, MR-PRESSO global test, and the leave-one-out analysis were conducted to detect the potential heterogeneity and horizontal pleiotropy. RESULTS: The present study found causalities between eight gut microbial genera and HDP. The HDP-associated gut microbial genera identified by MR analyses varied in different subtypes. Specifically, our study found causal associations of LachnospiraceaeUCG010, Olsenella, RuminococcaceaeUCG009, Ruminococcus2, Anaerotruncus, Bifidobacterium, and Intestinibacter with GH, of Eubacterium (ruminantium group), Eubacterium (ventriosum group), Methanobrevibacter, RuminococcaceaeUCG002, and Tyzzerella3 with PE, and of Dorea and RuminococcaceaeUCG010 with eclampsia, respectively. CONCLUSIONS: This study first applied the MR approach to detect the causal relationships between GM and specific HDP subtypes. Our findings may promote the prevention and treatment of HDP targeted on GM and provide valuable insights to understand the mechanism of HDP in different subtypes from the perspective of GM.

Mendelian randomization analyses support causal relationship between gut microbiota and childhood obesity.

Background: Childhood obesity (CO) is an increasing public health issue. Mounting evidence has shown that gut microbiota (GM) is closely related to CO. However, the causal association needs to be treated with caution due to confounding factors and reverse causation. Methods: Data were obtained from the Microbiome Genome Consortium for GM as well as the Early Growth Genetics Consortium for childhood obesity and childhood body mass index (CBMI). Inverse variance weighted, maximum likelihood, weighted median, and MR.RAPS methods were applied to examine the causal association. Then replication dataset was used to validate the results and reverse Mendelian randomization analysis was performed to confirm the causal direction. Additionally, sensitivity analyses including Cochran's Q statistics, MR-Egger intercept, MR-PRESSO global test, and the leave-one-out analysis were conducted to detect the potential heterogeneity and horizontal pleiotropy. Results: Our study found suggestive causal relationships between eight bacterial genera and the risk of childhood obesity (five for CO and four for CBMI). After validating the results in the replication dataset, we finally identified three childhood obesity-related GM including the genera Akkermansia, Intestinibacter, and Butyricimonas. Amongst these, the genus Akkermansia was both negatively associated with the risk of CO (OR = 0.574; 95% CI: 0.417, 0.789) and CBMI (ß = -0.172; 95% CI: -0.306, -0.039). Conclusions: In this study, we employed the MR approach to investigate the causal relationship between GM and CO, and discovered that the genus Akkermansia has a protective effect on both childhood obesity and BMI. Our findings may provide a potential strategy for preventing and intervening in CO, while also offering novel insights into the pathogenesis of CO from the perspective of GM.

Causal associations between gut microbiota, gut microbiota-derived metabolites, and cerebrovascular diseases: a multivariable Mendelian randomization study.

Background: Mounting evidence has demonstrated the associations between gut microbiota, gut microbiota-derived metabolites, and cerebrovascular diseases (CVDs). The major categories of CVD are ischemic stroke (IS), intracerebral hemorrhage (ICH), and subarachnoid hemorrhage (SAH). However, the causal relationship is still unclear. Methods: A two-sample Mendelian randomization (MR) study was conducted leveraging the summary data from genome-wide association studies. The inverse variance-weighted, maximum likelihood, weighted median, and MR.RAPS methods were performed to detect the causal relationship. Several sensitivity analyses were carried out to evaluate potential horizontal pleiotropy and heterogeneity. Finally, reverse MR analysis was conducted to examine the likelihood of reverse causality, and multivariable MR was performed to adjust the potential confounders. Results: We collected 1,505 host single nucleotide polymorphisms (SNPs) linked to 119 gut microbiota traits and 1,873 host SNPs associated with 81 gut metabolite traits as exposure data. Among these, three gut bacteria indicated an elevated risk of IS, two of ICH, and one of SAH. In contrast, five gut bacteria were associated with a reduced risk of IS, one with ICH, and one with SAH. Our study also demonstrated the potential causal associations between 11 gut microbiota-derived metabolites and CVD. Conclusions: This study provided evidence of the causal relationship between gut microbiota, gut microbiota-derived metabolites, and CVD, thereby offering novel perspectives on gut biomarkers and targeted prevention and treatment for CVD.

Investigating causal associations among gut microbiota, gut microbiota-derived metabolites, and gestational diabetes mellitus: a bidirectional Mendelian randomization study.

BACKGROUND: Previous studies have shown that gut microbiota (GM) and gut microbiota-derived metabolites are associated with gestational diabetes mellitus (GDM). However, the causal associations need to be treated with caution due to confounding factors and reverse causation. METHODS: This study obtained genetic variants from genome-wide association study including GM (N = 18,340), GM-derived metabolites (N = 7,824), and GDM (5,687 cases and 117,89 controls). To examine the causal association, several methods were utilized, including inverse variance weighted, maximum likelihood, weighted median, MR-Egger, and MR.RAPS. Additionally, reverse Mendelian Randomization (MR) analysis and multivariable MR were conducted to confirm the causal direction and account for potential confounders, respectively. Furthermore, sensitivity analyses were performed to identify any potential heterogeneity and horizontal pleiotropy. RESULTS: Greater abundance of Collinsella was detected to increase the risk of GDM. Our study also found suggestive associations among Coprobacter, Olsenella, Lachnoclostridium, Prevotella9, Ruminococcus2, Oscillibacte, and Methanobrevibacter with GDM. Besides, eight GM-derived metabolites were found to be causally associated with GDM. For the phenylalanine metabolism pathway, phenylacetic acid was found to be related to the risk of GDM. CONCLUSIONS: The study first used the MR approach to explore the causal associations among GM, GM-derived metabolites, and GDM. Our findings may contribute to the prevention and treatment strategies for GDM by targeting GM and metabolites, and offer novel insights into the underlying mechanism of the disease.