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      Two-sample Mendelian randomization to study the causal association between gut microbiota and atherosclerosis

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          Abstract

          Background

          According to some recent observational studies, the gut microbiota influences atherosclerosis via the gut microbiota-artery axis. However, the causal role of the gut microbiota in atherosclerosis remains unclear. Therefore, we used a Mendelian randomization (MR) strategy to try to dissect this causative link.

          Methods

          The biggest known genome-wide association study (GWAS) (n = 13,266) from the MiBioGen collaboration was used to provide summary data on the gut microbiota for a two-sample MR research. Data on atherosclerosis were obtained from publicly available GWAS data from the FinnGen consortium, including cerebral atherosclerosis (104 cases and 218,688 controls), coronary atherosclerosis (23,363 cases and 187,840 controls), and peripheral atherosclerosis (6631 cases and 162,201 controls). The causal link between gut microbiota and atherosclerosis was investigated using inverse variance weighting, MR-Egger, weighted median, weighted mode, and simple mode approaches, among which inverse variance weighting was the main research method. Cochran’s Q statistic was used to quantify the heterogeneity of instrumental variables (IVs), and the MR Egger intercept test was used to assess the pleiotropy of IVs.

          Results

          Inverse-variance-weighted (IVW) estimation showed that genus Ruminiclostridium 9 had a protective influence on cerebral atherosclerosis (OR = 0.10, 95% CI: 0.01–0.67, P = 0.018), while family Rikenellaceae (OR = 5.39, 95% CI: 1.50–19.37, P = 0.010), family Streptococcaceae (OR = 6.87, 95% CI: 1.60–29.49, P = 0.010), genus Paraprevotella (OR = 2.88, 95% CI: 1.18–7.05, P = 0.021), and genus Streptococcus (OR = 5.26, 95% CI: 1.28–21.61, P = 0.021) had pathogenic effects on cerebral atherosclerosis. For family Acidaminococcaceae (OR = 0.87, 95% CI: 0.76–0.99, P = 0.039), the genus Desulfovibrio (OR = 0.89, 95% CI: 0.80–1.00, P = 0.048), the genus RuminococcaceaeUCG010 (OR = 0.80, 95% CI: 0.69–0.94, P = 0.006), and the Firmicutes phyla (OR = 0.87, 95% CI: 0.77–0.98, P = 0.023) were protective against coronary atherosclerosis. However, the genus Catenibacterium (OR = 1.12, 95% CI: 1.00–1.24, P = 0.049) had a pathogenic effect on coronary atherosclerosis. Finally, class Actinobacteria (OR = 0.83, 95% CI: 0.69–0.99, P = 0.036), family Acidaminococcaceae (OR = 0.76, 95% CI: 0.61–0.94, P = 0.013), genus Coprococcus2 (OR = 0.76, 95% CI: 0.60–0.96, P = 0.022), and genus RuminococcaceaeUCG010 (OR = 0.65, 95% CI: 0.46–0.92, P = 0.013), these four microbiota have a protective effect on peripheral atherosclerosis. However, for the genus Lachnoclostridium (OR = 1.25, 95% CI: 1.01–1.56, P = 0.040) and the genus LachnospiraceaeUCG001 (OR = 1.22, 95% CI: 1.04–1.42, P = 0.016), there is a pathogenic role for peripheral atherosclerosis. No heterogeneity was found for instrumental variables, and no considerable horizontal pleiotropy was observed.

          Conclusion

          We discovered that the presence of probiotics and pathogens in the host is causally associated with atherosclerosis, and atherosclerosis at different sites is causally linked to specific gut microbiota. The specific gut microbiota associated with atherosclerosis identified by Mendelian randomization studies provides precise clinical targets for the treatment of atherosclerosis. In the future, we can further examine the gut microbiota’s therapeutic potential for atherosclerosis if we have a better grasp of the causal relationship between it and atherosclerosis.

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          Most cited references54

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          The MR-Base platform supports systematic causal inference across the human phenome

          Results from genome-wide association studies (GWAS) can be used to infer causal relationships between phenotypes, using a strategy known as 2-sample Mendelian randomization (2SMR) and bypassing the need for individual-level data. However, 2SMR methods are evolving rapidly and GWAS results are often insufficiently curated, undermining efficient implementation of the approach. We therefore developed MR-Base (http://www.mrbase.org): a platform that integrates a curated database of complete GWAS results (no restrictions according to statistical significance) with an application programming interface, web app and R packages that automate 2SMR. The software includes several sensitivity analyses for assessing the impact of horizontal pleiotropy and other violations of assumptions. The database currently comprises 11 billion single nucleotide polymorphism-trait associations from 1673 GWAS and is updated on a regular basis. Integrating data with software ensures more rigorous application of hypothesis-driven analyses and allows millions of potential causal relationships to be efficiently evaluated in phenome-wide association studies.
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            From Dietary Fiber to Host Physiology: Short-Chain Fatty Acids as Key Bacterial Metabolites.

            A compelling set of links between the composition of the gut microbiota, the host diet, and host physiology has emerged. Do these links reflect cause-and-effect relationships, and what might be their mechanistic basis? A growing body of work implicates microbially produced metabolites as crucial executors of diet-based microbial influence on the host. Here, we will review data supporting the diverse functional roles carried out by a major class of bacterial metabolites, the short-chain fatty acids (SCFAs). SCFAs can directly activate G-coupled-receptors, inhibit histone deacetylases, and serve as energy substrates. They thus affect various physiological processes and may contribute to health and disease.
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              Interpreting findings from Mendelian randomization using the MR-Egger method

              Mendelian randomization-Egger (MR-Egger) is an analysis method for Mendelian randomization using summarized genetic data. MR-Egger consists of three parts: (1) a test for directional pleiotropy, (2) a test for a causal effect, and (3) an estimate of the causal effect. While conventional analysis methods for Mendelian randomization assume that all genetic variants satisfy the instrumental variable assumptions, the MR-Egger method is able to assess whether genetic variants have pleiotropic effects on the outcome that differ on average from zero (directional pleiotropy), as well as to provide a consistent estimate of the causal effect, under a weaker assumption—the InSIDE (INstrument Strength Independent of Direct Effect) assumption. In this paper, we provide a critical assessment of the MR-Egger method with regard to its implementation and interpretation. While the MR-Egger method is a worthwhile sensitivity analysis for detecting violations of the instrumental variable assumptions, there are several reasons why causal estimates from the MR-Egger method may be biased and have inflated Type 1 error rates in practice, including violations of the InSIDE assumption and the influence of outlying variants. The issues raised in this paper have potentially serious consequences for causal inferences from the MR-Egger approach. We give examples of scenarios in which the estimates from conventional Mendelian randomization methods and MR-Egger differ, and discuss how to interpret findings in such cases. Electronic supplementary material The online version of this article (doi:10.1007/s10654-017-0255-x) contains supplementary material, which is available to authorized users.
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                Author and article information

                Contributors
                URI : https://loop.frontiersin.org/people/2211336Role: Role: Role: Role: Role:
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                Journal
                Front Immunol
                Front Immunol
                Front. Immunol.
                Frontiers in Immunology
                Frontiers Media S.A.
                1664-3224
                12 January 2024
                2023
                : 14
                : 1282072
                Affiliations
                [1] 1 Department of Cardiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology , Wuhan, China
                [2] 2 Hubei Key Laboratory of Biological Targeted Therapy, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology , Wuhan, China
                [3] 3 Hubei Provincial Engineering Research Center of Immunological Diagnosis and Therapy for Cardiovascular Diseases, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology , Wuhan, China
                [4] 4 Department of Cardiology, The First Affiliated Hospital, Shihezi University , Shihezi, China
                [5] 5 Department of Neurology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology , Wuhan, China
                Author notes

                Edited by: Yating Li, Zhejiang University, China

                Reviewed by: Georgia Damoraki, National and Kapodistrian University of Athens, Greece

                George Grant, University of Aberdeen, United Kingdom

                *Correspondence: Dazhu Li, lidazhuhp@ 123456sohu.com ; Jibin Lin, linjibin713@ 123456live.cn

                †These authors have contributed equally to this work and share first authorship

                Article
                10.3389/fimmu.2023.1282072
                10811052
                38283337
                2fab370a-a661-45b3-9a29-11e5ff17ee7d
                Copyright © 2024 Jiang, Yu, Lv, He, Zheng, Yang, Wang, Li and Lin

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 23 August 2023
                : 27 December 2023
                Page count
                Figures: 8, Tables: 1, Equations: 0, References: 54, Pages: 12, Words: 4878
                Funding
                Funded by: Tongji Medical College, Huazhong University of Science and Technology , doi 10.13039/501100013291;
                Award ID: 81700390
                The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This work was supported by the National Natural Science Foundation of China (Nos.82070317, 22274105 and 81700390 to Jibin Lin,82100298 to Bingjie Lv, and 82000428 to Boyuan Wang) and the National Key R&D Program of China (No.2017YFA0208000 to Shaolin He).
                Categories
                Immunology
                Original Research
                Custom metadata
                Microbial Immunology

                Immunology
                cerebral atherosclerosis,coronary atherosclerosis,gut microbiota,mendelian randomization,peripheral atherosclerosis

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