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      Gut dysbiosis in rheumatic diseases: A systematic review and meta-analysis of 92 observational studies

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          Summary

          Background

          Emerging evidence suggests that dysbiosis in gut microbiota may contribute to the occurrence or development of several rheumatic diseases. Since gut microbiota dysbiosis is potentially modifiable, it has been postulated to be a promising preventive or therapeutic target for rheumatic diseases. However, the current understanding on the potential associations between gut microbiota and rheumatic diseases is still inadequate. Therefore, we aimed to synthesise the accumulating evidence for the relation of gut microbiota to rheumatic diseases.

          Methods

          The PubMed, Embase and Cochrane Library were searched from inception to March 11, 2022 to include observational studies evaluating the associations between gut microbiota and rheumatic diseases. Standardised mean difference (SMD) of α-diversity indices between rheumatic diseases and controls were estimated using random-effects model. β-diversity indices and relative abundance of gut microbes were summarised qualitatively.

          Findings

          Of the included 92 studies (11,998 participants), 68 provided data for α-diversity. Taken together as a whole, decreases in α-diversity indices were consistently found in rheumatic diseases (observed species: SMD = −0.36, [95%CI = −0.63, −0.09]; Chao1: SMD = −0.57, [95%CI = −0.88, −0.26]; Shannon index: SMD = −0.33, [95%CI = −0.48, −0.17]; Simpson index: SMD = −0.32, [95%CI = −0.49, −0.14]). However, when specific rheumatic diseases were examined, decreases were only observed in rheumatoid arthritis (observed species: SMD = −0.51, [95%CI = −0.78, −0.24]; Shannon index: SMD = −0.31, [95%CI = −0.49, −0.13]; Simpson index: SMD = −0.31, [95%CI = −0.54, −0.08]), systemic lupus erythematosus (Chao1: SMD = −1.60, [95%CI = −2.54, −0.66]; Shannon index: SMD = −0.63, [95%CI = −1.08, −0.18]), gout (Simpson index: SMD = −0.64, [95%CI = −1.07, −0.22]) and fibromyalgia (Simpson index: SMD = −0.28, [95%CI = −0.44, −0.11]), whereas an increase was observed in systemic sclerosis (Shannon index: SMD = 1.25, [95%CI = 0.09, 2.41]). Differences with statistical significance in β-diversity were consistently reported in ankylosing spondylitis and IgG4-related diseases. Although little evidence of disease specificity of gut microbes was found, shared alterations of the depletion of anti-inflammatory butyrate-producing microbe (i.e., Faecalibacterium) and the enrichment of pro-inflammatory microbe (i.e., Streptococcus) were observed in rheumatoid arthritis, Sjögren's syndrome and systemic lupus erythematosus.

          Interpretation

          Gut microbiota dysbiosis was associated with rheumatic diseases, principally with potentially non-specific, shared alterations of microbes.

          Funding

          National Natural Science Foundation of China (81930071, 81902265, 82072502 and U21A20352).

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          Most cited references91

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          The PRISMA 2020 statement: an updated guideline for reporting systematic reviews

          The Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) statement, published in 2009, was designed to help systematic reviewers transparently report why the review was done, what the authors did, and what they found. Over the past decade, advances in systematic review methodology and terminology have necessitated an update to the guideline. The PRISMA 2020 statement replaces the 2009 statement and includes new reporting guidance that reflects advances in methods to identify, select, appraise, and synthesise studies. The structure and presentation of the items have been modified to facilitate implementation. In this article, we present the PRISMA 2020 27-item checklist, an expanded checklist that details reporting recommendations for each item, the PRISMA 2020 abstract checklist, and the revised flow diagrams for original and updated reviews.
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            Estimating the sample mean and standard deviation from the sample size, median, range and/or interquartile range

            Background In systematic reviews and meta-analysis, researchers often pool the results of the sample mean and standard deviation from a set of similar clinical trials. A number of the trials, however, reported the study using the median, the minimum and maximum values, and/or the first and third quartiles. Hence, in order to combine results, one may have to estimate the sample mean and standard deviation for such trials. Methods In this paper, we propose to improve the existing literature in several directions. First, we show that the sample standard deviation estimation in Hozo et al.’s method (BMC Med Res Methodol 5:13, 2005) has some serious limitations and is always less satisfactory in practice. Inspired by this, we propose a new estimation method by incorporating the sample size. Second, we systematically study the sample mean and standard deviation estimation problem under several other interesting settings where the interquartile range is also available for the trials. Results We demonstrate the performance of the proposed methods through simulation studies for the three frequently encountered scenarios, respectively. For the first two scenarios, our method greatly improves existing methods and provides a nearly unbiased estimate of the true sample standard deviation for normal data and a slightly biased estimate for skewed data. For the third scenario, our method still performs very well for both normal data and skewed data. Furthermore, we compare the estimators of the sample mean and standard deviation under all three scenarios and present some suggestions on which scenario is preferred in real-world applications. Conclusions In this paper, we discuss different approximation methods in the estimation of the sample mean and standard deviation and propose some new estimation methods to improve the existing literature. We conclude our work with a summary table (an Excel spread sheet including all formulas) that serves as a comprehensive guidance for performing meta-analysis in different situations. Electronic supplementary material The online version of this article (doi:10.1186/1471-2288-14-135) contains supplementary material, which is available to authorized users.
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              A metagenome-wide association study of gut microbiota in type 2 diabetes.

              Assessment and characterization of gut microbiota has become a major research area in human disease, including type 2 diabetes, the most prevalent endocrine disease worldwide. To carry out analysis on gut microbial content in patients with type 2 diabetes, we developed a protocol for a metagenome-wide association study (MGWAS) and undertook a two-stage MGWAS based on deep shotgun sequencing of the gut microbial DNA from 345 Chinese individuals. We identified and validated approximately 60,000 type-2-diabetes-associated markers and established the concept of a metagenomic linkage group, enabling taxonomic species-level analyses. MGWAS analysis showed that patients with type 2 diabetes were characterized by a moderate degree of gut microbial dysbiosis, a decrease in the abundance of some universal butyrate-producing bacteria and an increase in various opportunistic pathogens, as well as an enrichment of other microbial functions conferring sulphate reduction and oxidative stress resistance. An analysis of 23 additional individuals demonstrated that these gut microbial markers might be useful for classifying type 2 diabetes.
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                Author and article information

                Contributors
                Journal
                eBioMedicine
                EBioMedicine
                eBioMedicine
                Elsevier
                2352-3964
                17 May 2022
                June 2022
                17 May 2022
                : 80
                : 104055
                Affiliations
                [a ]Department of Orthopaedics, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha, Hunan 410008, China
                [b ]Health Management Center, Xiangya Hospital, Central South University, Changsha, China
                [c ]Hunan Key Laboratory of Joint Degeneration and Injury, Changsha, China
                [d ]University of Nottingham, Nottingham, UK
                [e ]Pain Centre Versus Arthritis UK, Nottingham, UK
                [f ]Division of Rheumatology, Allergy, and Immunology, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, USA
                [g ]The Mongan Institute, Massachusetts General Hospital, Harvard Medical School, Boston, USA
                [h ]National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
                Author notes
                [* ]Corresponding authors at: Department of Orthopaedics, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha, Hunan 410008, China. lei_guanghua@ 123456csu.edu.cn zengchao@ 123456csu.edu.cn
                [1]

                These authors contributed equally to this work.

                Article
                S2352-3964(22)00236-5 104055
                10.1016/j.ebiom.2022.104055
                9120231
                35594658
                c2abd14e-2d22-47d5-b2a9-33e6017f7a1b
                © 2022 The Author(s)

                This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

                History
                : 1 February 2022
                : 21 April 2022
                : 28 April 2022
                Categories
                Articles

                gut microbiota,gut dysbiosis,rheumatic diseases,meta-analysis

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