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      The causal relationship between gut microbiota and type 2 diabetes: a two-sample Mendelian randomized study

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          Abstract

          Background

          Type 2 diabetes mellitus (T2DM) is a commonly observed metabolic anomaly globally, and as of the present time, there's no recognized solution. There is an increasing body of evidence from numerous observational studies indicating a significant correlation between gut flora and metabolic disease progression, particularly in relation to T2DM. Despite this, the direct impact of gut microbiota on T2DM isn't fully understood yet.

          Methods

          The summary statistical figures for intestinal microbiota were sourced from the MiBioGen consortium, while the summary statistical data for T2DM were gathered from the Genome-Wide Association Studies (GWAS) database. These datasets were used to execute a two-sample Mendelian randomization (MR) investigation. The Inverse Variance Weighted (IVW), Maximum Likelihood, MR-Egger, Weighted Median, and Weighted Models strategies were employed to assess the impact of gut microbiota on T2DM. Findings were primarily obtained using the IVW technique. Techniques like MR-Egger were employed to identify the occurrence of horizontal pleiotropy among instrumental variables. Meanwhile, Cochran's Q statistical measures were utilized to assess the variability or heterogeneity within these instrumental variables.

          Results

          The outcomes from the IVW analysis demonstrated that the genus Alistipes (OR = 0.998, 95% confidence interval: 0.996–1.000, and P = 0.038), genus Allisonella (OR = 0.998, 95% confidence interval: 0.997-0.999, P = 0.033), genus Flavonifractor (OR = 0.995, 95% confidence interval: 0.993–0.998, P = 3.78 × 10 −3), and genus Haemophilus (OR = 0.995, 95% confidence interval: 0.993–0.998, P = 8.08 × 10 −3) all acted as defense elements against type 2 diabetes. Family Clostridiaceae1 (OR = 1.003, 95% confidence interval: 1.001–1.005, P = 0.012), family Coriobacteriaceae (OR = 1.0025, 95% confidence interval: 1.000–1.005, P = 0.043), genus Actinomyces (OR = 1.003,95% confidence interval: 1.001–1.005, P = 4.38 × 10 −3), genus Candidatus Soleaferrea (OR = 1.001,95% confidence interval: 1.000–1.002 P = 0.012) were risk factors for type 2 diabetes. False Discovery Rate correction was performed with finding that genus. Allisonella, genus .Alistipes, family Coriobacteriaceaeand T2DM no longer displayed a significant causal association. In addition, no significant heterogeneity or horizontal pleiotropy was found for instrumental variable.

          Conclusion

          This MR study relies on genetic variation tools to confirm the causal effect of genus Flavonifractor, genus Haemophilus, family Clostridiaceae1, genus Actinomyces and genus Candidatus Soleaferrea on T2DM in the gut microbiome, providing new directions and strategies for the treatment and early screening of T2DM, which carries significant clinical relevance. To develop new biomarkers and better understand targeted prevention strategies for T2DM, further comprehensive investigations are required into the protective and detrimental mechanisms exerted by these five genera against T2DM.

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          Most cited references43

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          Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression

          Background: The number of Mendelian randomization analyses including large numbers of genetic variants is rapidly increasing. This is due to the proliferation of genome-wide association studies, and the desire to obtain more precise estimates of causal effects. However, some genetic variants may not be valid instrumental variables, in particular due to them having more than one proximal phenotypic correlate (pleiotropy). Methods: We view Mendelian randomization with multiple instruments as a meta-analysis, and show that bias caused by pleiotropy can be regarded as analogous to small study bias. Causal estimates using each instrument can be displayed visually by a funnel plot to assess potential asymmetry. Egger regression, a tool to detect small study bias in meta-analysis, can be adapted to test for bias from pleiotropy, and the slope coefficient from Egger regression provides an estimate of the causal effect. Under the assumption that the association of each genetic variant with the exposure is independent of the pleiotropic effect of the variant (not via the exposure), Egger’s test gives a valid test of the null causal hypothesis and a consistent causal effect estimate even when all the genetic variants are invalid instrumental variables. Results: We illustrate the use of this approach by re-analysing two published Mendelian randomization studies of the causal effect of height on lung function, and the causal effect of blood pressure on coronary artery disease risk. The conservative nature of this approach is illustrated with these examples. Conclusions: An adaption of Egger regression (which we call MR-Egger) can detect some violations of the standard instrumental variable assumptions, and provide an effect estimate which is not subject to these violations. The approach provides a sensitivity analysis for the robustness of the findings from a Mendelian randomization investigation.
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            WITHDRAWN: Global and regional diabetes prevalence estimates for 2019 and projections for 2030 and 2045: results from the International Diabetes Federation Diabetes Atlas, 9th edition

            To provide global estimates of diabetes prevalence for 2019 and projections for 2030 and 2045.
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              Mendelian randomization: genetic anchors for causal inference in epidemiological studies

              Observational epidemiological studies are prone to confounding, reverse causation and various biases and have generated findings that have proved to be unreliable indicators of the causal effects of modifiable exposures on disease outcomes. Mendelian randomization (MR) is a method that utilizes genetic variants that are robustly associated with such modifiable exposures to generate more reliable evidence regarding which interventions should produce health benefits. The approach is being widely applied, and various ways to strengthen inference given the known potential limitations of MR are now available. Developments of MR, including two-sample MR, bidirectional MR, network MR, two-step MR, factorial MR and multiphenotype MR, are outlined in this review. The integration of genetic information into population-based epidemiological studies presents translational opportunities, which capitalize on the investment in genomic discovery research.
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                Author and article information

                Contributors
                URI : http://loop.frontiersin.org/people/2370491/overviewRole: Role: Role: Role:
                Role: Role: Role: Role: Role:
                Role: Role: Role: Role:
                URI : http://loop.frontiersin.org/people/2444416/overviewRole: Role: Role: Role: Role:
                Journal
                Front Public Health
                Front Public Health
                Front. Public Health
                Frontiers in Public Health
                Frontiers Media S.A.
                2296-2565
                22 September 2023
                2023
                : 11
                : 1255059
                Affiliations
                [1] 1School of Medical Laboratory, Weifang Medical College , Weifang, China
                [2] 2Department of Blood Transfusion, The 960th Hospital of the PLA Jonit Logistics Support Force , Jinan, China
                [3] 3Department of General Medicine, The 960th Hospital of the PLA Jonit Logistics Support Force , Jinan, China
                [4] 4School of Mathematics and Statistics, Beijing Technology and Business University , Beijing, China
                Author notes

                Edited by: Raffaella Maria Gadaleta, University of Bari Aldo Moro, Italy

                Reviewed by: Cosmin Mihai Vesa, University of Oradea, Romania; Ryan Russell, The University of Texas Rio Grande Valley, United States

                *Correspondence: Xiangyan Huang xiangyan73@ 123456aliyun.com
                Article
                10.3389/fpubh.2023.1255059
                10556527
                37808975
                3eb60057-a716-4df8-bf00-75a17e6e0f56
                Copyright © 2023 Sun, Gao, Wu and Huang.

                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
                : 08 July 2023
                : 04 September 2023
                Page count
                Figures: 4, Tables: 1, Equations: 1, References: 43, Pages: 9, Words: 5270
                Categories
                Public Health
                Original Research
                Custom metadata
                Clinical Diabetes

                mendelian randomized study,gut microbiota,type 2 diabetes,causal inference,genetic variation

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