肠道微生物群与心血管疾病因果关系的评估:双向孟德尔随机分析。

IF 4 3区 生物学 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY Biodata Mining Pub Date : 2024-02-26 DOI:10.1186/s13040-024-00356-2
Xiao-Ce Dai, Yi Yu, Si-Yu Zhou, Shuo Yu, Mei-Xiang Xiang, Hong Ma
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引用次数: 0

摘要

背景:以往的研究表明,肠道微生物群与心血管疾病(CVDs)之间存在关联。然而,其背后的因果关系仍不清楚。本研究旨在阐明肠道微生物群与心血管疾病之间的因果关系,并探讨肠道微生物群在心血管疾病中的致病作用:在这项双样本孟德尔随机研究中,我们使用了来自公开全基因组关联研究的遗传工具,包括与肠道微生物群(n = 14,306 个)和心血管疾病(n = 2,207,591 个)相关的单核苷酸多态性(SNPs)。我们采用了多种统计分析方法,包括反方差加权、MR Egger、加权中位数、MR 多态残差和离群值以及撇除法,来估计肠道微生物群与心血管疾病之间的因果关系。此外,我们还进行了多重分析,以评估水平多义性和异质性:GWAS汇总数据来自2,221,897名成人和青少年参与者的汇总样本。我们的研究结果表明,特定的肠道微生物群对心血管疾病具有保护或有害作用。值得注意的是,霍华德氏菌(OR = 0.955,95% CI:0.913-0.999,P = .05)、肠杆菌(OR = 0.908,95% CI:0.831-0.993,P = .03)、Lachnospiraceae(NK4A136 组)(OR = 0.904,95% CI:0.841-0.973,P = .007)、Turisibacter(OR = 0.904,95% CI:0.838-0.976,P = .01)、Holdemania(OR,0.898;95% CI:0.810-0.995,P = .04)和Odoribacter(OR,0.835;95% CI:0.710-0.993,P = .04)对心房颤动具有保护性因果效应,而其他微生物群则具有不利的因果效应。在冠状动脉疾病、心肌梗塞、缺血性中风和高血压方面也观察到类似的效应。此外,反向孟德尔随机分析显示,心房颤动和缺血性中风对某些肠道微生物群具有因果效应:我们的研究强调了肠道微生物群在心血管疾病中的重要性,并支持了增加益生菌数量或减少有害细菌数量可预防特定心血管疾病的假设。不过,要将这些发现转化为临床实践,还需要进一步的研究。
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Assessment of the causal relationship between gut microbiota and cardiovascular diseases: a bidirectional Mendelian randomization analysis.

Background: Previous studies have shown an association between gut microbiota and cardiovascular diseases (CVDs). However, the underlying causal relationship remains unclear. This study aims to elucidate the causal relationship between gut microbiota and CVDs and to explore the pathogenic role of gut microbiota in CVDs.

Methods: In this two-sample Mendelian randomization study, we used genetic instruments from publicly available genome-wide association studies, including single-nucleotide polymorphisms (SNPs) associated with gut microbiota (n = 14,306) and CVDs (n = 2,207,591). We employed multiple statistical analysis methods, including inverse variance weighting, MR Egger, weighted median, MR pleiotropic residuals and outliers, and the leave-one-out method, to estimate the causal relationship between gut microbiota and CVDs. Additionally, we conducted multiple analyses to assess horizontal pleiotropy and heterogeneity.

Results: GWAS summary data were available from a pooled sample of 2,221,897 adult and adolescent participants. Our findings indicated that specific gut microbiota had either protective or detrimental effects on CVDs. Notably, Howardella (OR = 0.955, 95% CI: 0.913-0.999, P = .05), Intestinibacter (OR = 0.908, 95% CI:0.831-0.993, P = .03), Lachnospiraceae (NK4A136 group) (OR = 0.904, 95% CI:0.841-0.973, P = .007), Turicibacter (OR = 0.904, 95% CI: 0.838-0.976, P = .01), Holdemania (OR, 0.898; 95% CI: 0.810-0.995, P = .04) and Odoribacter (OR, 0.835; 95% CI: 0.710-0.993, P = .04) exhibited a protective causal effect on atrial fibrillation, while other microbiota had adverse causal effects. Similar effects were observed with respect to coronary artery disease, myocardial infarction, ischemic stroke, and hypertension. Furthermore, reversed Mendelian randomization analyses revealed that atrial fibrillation and ischemic stroke had causal effects on certain gut microbiotas.

Conclusion: Our study underscored the importance of gut microbiota in the context of CVDs and lent support to the hypothesis that increasing the abundance of probiotics or decreasing the abundance of harmful bacterial populations may offer protection against specific CVDs. Nevertheless, further research is essential to translate these findings into clinical practice.

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来源期刊
Biodata Mining
Biodata Mining MATHEMATICAL & COMPUTATIONAL BIOLOGY-
CiteScore
7.90
自引率
0.00%
发文量
28
审稿时长
23 weeks
期刊介绍: BioData Mining is an open access, open peer-reviewed journal encompassing research on all aspects of data mining applied to high-dimensional biological and biomedical data, focusing on computational aspects of knowledge discovery from large-scale genetic, transcriptomic, genomic, proteomic, and metabolomic data. Topical areas include, but are not limited to: -Development, evaluation, and application of novel data mining and machine learning algorithms. -Adaptation, evaluation, and application of traditional data mining and machine learning algorithms. -Open-source software for the application of data mining and machine learning algorithms. -Design, development and integration of databases, software and web services for the storage, management, retrieval, and analysis of data from large scale studies. -Pre-processing, post-processing, modeling, and interpretation of data mining and machine learning results for biological interpretation and knowledge discovery.
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