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      Choline consumption reduces CVD risk via body composition modification

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          Abstract

          Despite extensive research on the relationship between choline and cardiovascular disease (CVD), conflicting findings have been reported. We aim to investigate the relationship between choline and CVD. Our analysis screened a retrospective cohort study of 14,663 participants from the National Health and Nutrition Examination Survey conducted between 2013 and 2018. Propensity score matching and restricted cubic splines was used to access the association between choline intake and the risk of CVD. A two-sample Mendelian randomization (MR) analysis was conducted to examine the potential causality. Additionally, sets of single cell RNA-sequencing data were extracted and analyzed, in order to explore the role of choline metabolism pathway in the progression and severity of the CVD and the underlying potential mechanisms involved. The adjusted odds ratios and 95% confidence intervals for stroke were 0.72 (0.53–0.98; p = 0.035) for quartile 3 and 0.54 (0.39–0.75; p < 0.001) for quartile 4. A stratified analysis revealed that the relationship between choline intake and stroke varied among different body mass index and waist circumference groups. The results of MR analysis showed that choline and phosphatidylcholine had a predominantly negative causal effect on fat percentage, fat mass, and fat-free mass, while glycine had opposite effects. Results from bioinformatics analysis revealed that alterations in the choline metabolism pathway following stroke may be associated with the prognosis. Our study indicated that the consumption of an appropriate quantity of choline in the diet may help to protect against CVD and the effect may be choline-mediated, resulting in a healthier body composition. Furthermore, the regulation of the choline metabolism pathway following stroke may be a promising therapeutic target.

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          Gene Ontology: tool for the unification of biology

          Genomic sequencing has made it clear that a large fraction of the genes specifying the core biological functions are shared by all eukaryotes. Knowledge of the biological role of such shared proteins in one organism can often be transferred to other organisms. The goal of the Gene Ontology Consortium is to produce a dynamic, controlled vocabulary that can be applied to all eukaryotes even as knowledge of gene and protein roles in cells is accumulating and changing. To this end, three independent ontologies accessible on the World-Wide Web (http://www.geneontology.org) are being constructed: biological process, molecular function and cellular component.
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            Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources.

            DAVID bioinformatics resources consists of an integrated biological knowledgebase and analytic tools aimed at systematically extracting biological meaning from large gene/protein lists. This protocol explains how to use DAVID, a high-throughput and integrated data-mining environment, to analyze gene lists derived from high-throughput genomic experiments. The procedure first requires uploading a gene list containing any number of common gene identifiers followed by analysis using one or more text and pathway-mining tools such as gene functional classification, functional annotation chart or clustering and functional annotation table. By following this protocol, investigators are able to gain an in-depth understanding of the biological themes in lists of genes that are enriched in genome-scale studies.
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              Metascape provides a biologist-oriented resource for the analysis of systems-level datasets

              A critical component in the interpretation of systems-level studies is the inference of enriched biological pathways and protein complexes contained within OMICs datasets. Successful analysis requires the integration of a broad set of current biological databases and the application of a robust analytical pipeline to produce readily interpretable results. Metascape is a web-based portal designed to provide a comprehensive gene list annotation and analysis resource for experimental biologists. In terms of design features, Metascape combines functional enrichment, interactome analysis, gene annotation, and membership search to leverage over 40 independent knowledgebases within one integrated portal. Additionally, it facilitates comparative analyses of datasets across multiple independent and orthogonal experiments. Metascape provides a significantly simplified user experience through a one-click Express Analysis interface to generate interpretable outputs. Taken together, Metascape is an effective and efficient tool for experimental biologists to comprehensively analyze and interpret OMICs-based studies in the big data era.

                Author and article information

                Contributors
                yangqwmlys@163.com
                huchrong@tmmu.edu.cn
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                12 July 2024
                12 July 2024
                2024
                : 14
                : 16152
                Affiliations
                [1 ]GRID grid.416208.9, ISNI 0000 0004 1757 2259, Department of Neurosurgery and Key Laboratory of Neurotrauma, , Southwest Hospital, Army Medical University (Third Military Medical University), ; Chongqing, China
                [2 ]GRID grid.414048.d, ISNI 0000 0004 1799 2720, Department of Cardiology, , Daping Hospital, Army Medical University (Third Military Medical University), ; Chongqing, China
                [3 ]Department of Neurology, Xinqiao Hospital and The Second Affiliated Hospital, Army Medical University (Third Military Medical University), ( https://ror.org/02d217z27) Chongqing, China
                Article
                66039
                10.1038/s41598-024-66039-4
                11245612
                38997295
                7a6e37f5-9a91-4919-a784-4d703382c470
                © The Author(s) 2024

                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
                : 28 February 2024
                : 26 June 2024
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100013254, National College Students Innovation and Entrepreneurship Training Program;
                Award ID: 202290031034
                Award Recipient :
                Funded by: Joint project of Chongqing Health Commission and Science and Technology Bureau
                Award ID: 2023GGXM003
                Award Recipient :
                Categories
                Article
                Custom metadata
                © Springer Nature Limited 2024

                Uncategorized
                cardiology,neurology,risk factors
                Uncategorized
                cardiology, neurology, risk factors

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