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      Mendelian randomization analyses reveal causal relationships between the human microbiome and longevity

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          Abstract

          Although recent studies have revealed the association between the human microbiome especially gut microbiota and longevity, their causality remains unclear. Here, we assess the causal relationships between the human microbiome (gut and oral microbiota) and longevity, by leveraging bidirectional two-sample Mendelian randomization (MR) analyses based on genome-wide association studies (GWAS) summary statistics of the gut and oral microbiome from the 4D-SZ cohort and longevity from the CLHLS cohort. We found that some disease-protected gut microbiota such as Coriobacteriaceae and Oxalobacter as well as the probiotic Lactobacillus amylovorus were related to increased odds of longevity, whereas the other gut microbiota such as colorectal cancer pathogen Fusobacterium nucleatum, Coprococcus, Streptococcus, Lactobacillus, and Neisseria were negatively associated with longevity. The reverse MR analysis further revealed genetically longevous individuals tended to have higher abundances of Prevotella and Paraprevotella but lower abundances of Bacteroides and Fusobacterium species. Few overlaps of gut microbiota-longevity interactions were identified across different populations. We also identified abundant links between the oral microbiome and longevity. The additional analysis suggested that centenarians genetically had a lower gut microbial diversity, but no difference in oral microbiota. Our findings strongly implicate these bacteria to play a role in human longevity and underscore the relocation of commensal microbes among different body sites that would need to be monitored for long and healthy life.

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          Consistent Estimation in Mendelian Randomization with Some Invalid Instruments Using a Weighted Median Estimator

          ABSTRACT Developments in genome‐wide association studies and the increasing availability of summary genetic association data have made application of Mendelian randomization relatively straightforward. However, obtaining reliable results from a Mendelian randomization investigation remains problematic, as the conventional inverse‐variance weighted method only gives consistent estimates if all of the genetic variants in the analysis are valid instrumental variables. We present a novel weighted median estimator for combining data on multiple genetic variants into a single causal estimate. This estimator is consistent even when up to 50% of the information comes from invalid instrumental variables. In a simulation analysis, it is shown to have better finite‐sample Type 1 error rates than the inverse‐variance weighted method, and is complementary to the recently proposed MR‐Egger (Mendelian randomization‐Egger) regression method. In analyses of the causal effects of low‐density lipoprotein cholesterol and high‐density lipoprotein cholesterol on coronary artery disease risk, the inverse‐variance weighted method suggests a causal effect of both lipid fractions, whereas the weighted median and MR‐Egger regression methods suggest a null effect of high‐density lipoprotein cholesterol that corresponds with the experimental evidence. Both median‐based and MR‐Egger regression methods should be considered as sensitivity analyses for Mendelian randomization investigations with multiple genetic variants.
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            Diet rapidly and reproducibly alters the human gut microbiome

            Long-term diet influences the structure and activity of the trillions of microorganisms residing in the human gut 1–5 , but it remains unclear how rapidly and reproducibly the human gut microbiome responds to short-term macronutrient change. Here, we show that the short-term consumption of diets composed entirely of animal or plant products alters microbial community structure and overwhelms inter-individual differences in microbial gene expression. The animal-based diet increased the abundance of bile-tolerant microorganisms (Alistipes, Bilophila, and Bacteroides) and decreased the levels of Firmicutes that metabolize dietary plant polysaccharides (Roseburia, Eubacterium rectale, and Ruminococcus bromii). Microbial activity mirrored differences between herbivorous and carnivorous mammals 2 , reflecting trade-offs between carbohydrate and protein fermentation. Foodborne microbes from both diets transiently colonized the gut, including bacteria, fungi, and even viruses. Finally, increases in the abundance and activity of Bilophila wadsworthia on the animal-based diet support a link between dietary fat, bile acids, and the outgrowth of microorganisms capable of triggering inflammatory bowel disease 6 . In concert, these results demonstrate that the gut microbiome can rapidly respond to altered diet, potentially facilitating the diversity of human dietary lifestyles.
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              Mendelian Randomization Analysis With Multiple Genetic Variants Using Summarized Data

              Genome-wide association studies, which typically report regression coefficients summarizing the associations of many genetic variants with various traits, are potentially a powerful source of data for Mendelian randomization investigations. We demonstrate how such coefficients from multiple variants can be combined in a Mendelian randomization analysis to estimate the causal effect of a risk factor on an outcome. The bias and efficiency of estimates based on summarized data are compared to those based on individual-level data in simulation studies. We investigate the impact of gene–gene interactions, linkage disequilibrium, and ‘weak instruments’ on these estimates. Both an inverse-variance weighted average of variant-specific associations and a likelihood-based approach for summarized data give similar estimates and precision to the two-stage least squares method for individual-level data, even when there are gene–gene interactions. However, these summarized data methods overstate precision when variants are in linkage disequilibrium. If the P-value in a linear regression of the risk factor for each variant is less than , then weak instrument bias will be small. We use these methods to estimate the causal association of low-density lipoprotein cholesterol (LDL-C) on coronary artery disease using published data on five genetic variants. A 30% reduction in LDL-C is estimated to reduce coronary artery disease risk by 67% (95% CI: 54% to 76%). We conclude that Mendelian randomization investigations using summarized data from uncorrelated variants are similarly efficient to those using individual-level data, although the necessary assumptions cannot be so fully assessed.
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                Author and article information

                Contributors
                junxiamin@zju.edu.cn
                zengyi@nsd.pku.edu.cn
                jiahuijue@genomics.cn
                houyong@genomics.cn
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                29 March 2023
                29 March 2023
                2023
                : 13
                : 5127
                Affiliations
                [1 ]GRID grid.410726.6, ISNI 0000 0004 1797 8419, College of Life Sciences, , University of Chinese Academy of Sciences, ; Beijing, 100049 China
                [2 ]GRID grid.21155.32, ISNI 0000 0001 2034 1839, BGI-Shenzhen, ; Shenzhen, 518083 China
                [3 ]GRID grid.13402.34, ISNI 0000 0004 1759 700X, James D. Watson Institute of Genome Sciences, ; Hangzhou, 310058 China
                [4 ]GRID grid.5254.6, ISNI 0000 0001 0674 042X, Department of Biology, , University of Copenhagen, ; Universitetsparken 13, 2100 Copenhagen, Denmark
                [5 ]GRID grid.13402.34, ISNI 0000 0004 1759 700X, School of Medicine, The First Affiliated Hospital, Institute of Translational Medicine, , Zhejiang University, ; Hangzhou, China
                [6 ]GRID grid.11135.37, ISNI 0000 0001 2256 9319, Center for Healthy Aging and Development Studies, National School of Development, Raissun Institute for Advanced Studies, , Peking University, ; Beijing, China
                [7 ]GRID grid.8547.e, ISNI 0000 0001 0125 2443, Greater Bay Area Institute of Precision Medicine (Guangzhou), , Fudan University, ; Shanghai, China
                Article
                31115
                10.1038/s41598-023-31115-8
                10052271
                36991009
                d3711e5d-08cc-4570-a4d2-d6b4b576f9fa
                © The Author(s) 2023

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 29 September 2022
                : 7 March 2023
                Funding
                Funded by: National Natural Sciences Foundation of China
                Award ID: 32200548
                Funded by: FundRef http://dx.doi.org/10.13039/501100012166, National Key Research and Development Program of China;
                Award ID: 2018YFC2000400
                Categories
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                © The Author(s) 2023

                Uncategorized
                genetic association study,genomics
                Uncategorized
                genetic association study, genomics

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