8
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Toward an improved definition of a healthy microbiome for healthy aging

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          The gut microbiome is a modifier of disease risk because it interacts with nutrition, metabolism, immunity and infection. Aging-related health loss has been correlated with transition to different microbiome states. Microbiome summary indices including alpha diversity are apparently useful to describe these states but belie taxonomic differences that determine biological importance. We analyzed 21,000 fecal microbiomes from seven data repositories, across five continents spanning participant ages 18–107 years, revealing that microbiome diversity and uniqueness correlate with aging, but not healthy aging. Among summary statistics tested, only Kendall uniqueness accurately reflects loss of the core microbiome and the abundance and ranking of disease-associated and health-associated taxa. Increased abundance of these disease-associated taxa and depletion of a coabundant subset of health-associated taxa are a generic feature of aging. These alterations are stronger correlates of unhealthy aging than most microbiome summary statistics and thus help identify better targets for therapeutic modulation of the microbiome.

          Abstract

          The authors analyze microbiome profiles from several public repositories to identify the higher-level indices that best reflect the abundance and ranking of disease-associated and health-associated gut microbes and that may help identify targets for therapeutic modulation.

          Related collections

          Most cited references45

          • Record: found
          • Abstract: found
          • Article: not found

          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.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: found
            Is Open Access

            Microbiome Datasets Are Compositional: And This Is Not Optional

            Datasets collected by high-throughput sequencing (HTS) of 16S rRNA gene amplimers, metagenomes or metatranscriptomes are commonplace and being used to study human disease states, ecological differences between sites, and the built environment. There is increasing awareness that microbiome datasets generated by HTS are compositional because they have an arbitrary total imposed by the instrument. However, many investigators are either unaware of this or assume specific properties of the compositional data. The purpose of this review is to alert investigators to the dangers inherent in ignoring the compositional nature of the data, and point out that HTS datasets derived from microbiome studies can and should be treated as compositions at all stages of analysis. We briefly introduce compositional data, illustrate the pathologies that occur when compositional data are analyzed inappropriately, and finally give guidance and point to resources and examples for the analysis of microbiome datasets using compositional data analysis.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: found
              Is Open Access

              Role of the gut microbiota in nutrition and health

              Ana M Valdes and colleagues discuss strategies for modulating the gut microbiota through diet and probiotics
                Bookmark

                Author and article information

                Contributors
                pwotoole@ucc.ie
                Journal
                Nat Aging
                Nat Aging
                Nature Aging
                Nature Publishing Group US (New York )
                2662-8465
                17 November 2022
                17 November 2022
                2022
                : 2
                : 11
                : 1054-1069
                Affiliations
                [1 ]GRID grid.7872.a, ISNI 0000000123318773, APC Microbiome Ireland, University College Cork, , National University of Ireland, ; Cork, Ireland
                [2 ]GRID grid.7872.a, ISNI 0000000123318773, School of Microbiology, University College Cork, , National University of Ireland, ; Cork, Ireland
                [3 ]GRID grid.7872.a, ISNI 0000000123318773, Department of Medicine, University College Cork, , National University of Ireland, ; Cork, Ireland
                [4 ]GRID grid.454294.a, ISNI 0000 0004 1773 2689, Present Address: Department of Computational Biology, , Indraprastha Institute of Information Technology, ; New Delhi, India
                Author information
                http://orcid.org/0000-0001-9570-0365
                http://orcid.org/0000-0001-5377-0824
                Article
                306
                10.1038/s43587-022-00306-9
                10154212
                37118093
                69520dc7-7d83-4deb-9bb7-a3bb2673bac9
                © The Author(s) 2022

                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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 30 July 2021
                : 4 October 2022
                Funding
                Funded by: FundRef https://doi.org/10.13039/501100001602, Science Foundation Ireland (SFI);
                Award ID: 12/RC/2273_P2
                Award ID: 12/RC/2273_P2
                Award Recipient :
                Categories
                Analysis
                Custom metadata
                © The Author(s), under exclusive licence to Springer Nature America, Inc. 2022

                microbiology,computational biology and bioinformatics,medical research,ageing

                Comments

                Comment on this article