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      Combinational Biomarkers for Atrial Fibrillation Derived from Atrial Appendage and Plasma Metabolomics Analysis

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          Abstract

          Atrial fibrillation (AF) is one of the most common types of arrhythmias and often leads to clinical complications. The objectives of this study were to offer insights into the metabolites of AF and to determine biomarkers for AF diagnosis or prediction. Sixty atrial appendage samples (AF group: 30; non-AF group: 30) and 163 plasma samples (AF group: 48; non-AF group: 115) from 49 AF patients and 116 non-AF patients were subjected to liquid chromatography positive ion electrospray ionization tandem mass spectrometry (LC-ESI-MS/MS) metabolomics analysis. Consequently, 24 metabolites in atrial appendage samples and 24 metabolites in plasma samples were found to reflect metabolic differences between AF and non-AF patients (variable importance in projection (VIP) ≥ 1, P ≤ 0.05). Five identical metabolites including creatinine, D-glutamic acid, choline, hypoxanthine, and niacinamide (VIP ≥ 1.5, P < 0.01, FDR < 0.05) in atrial appendage and plasma samples were considered prominent features of AF patients, and the D-glutamine and D-glutamate metabolic pathway was also identified as a feature of AF patients. Finally, in plasma samples, the combination of D-glutamic acid, creatinine, and choline had an AUC value of 0.927 (95% CI: 0.875–0.979, P < 0.001) and displayed 90.5% sensitivity and 83.3% specificity; this group of metabolites was thus defined as a combinational biomarker for the recognition of AF and non-AF patients.

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          Most cited references37

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          Independent risk factors for atrial fibrillation in a population-based cohort. The Framingham Heart Study.

          To determine the independent risk factors for atrial fibrillation. Cohort study. The Framingham Heart Study. A total of 2090 men and 2641 women members of the original cohort, free of a history of atrial fibrillation, between the ages of 55 and 94 years. Sex-specific multiple logistic regression models to identify independent risk factors for atrial fibrillation, including age, smoking, diabetes, electrocardiographic left ventricular hypertrophy, hypertension, myocardial infarction, congestive heart failure, and valve disease. During up to 38 years of follow-up, 264 men and 298 women developed atrial fibrillation. After adjusting for age and other risk factors for atrial fibrillation, men had a 1.5 times greater risk of developing atrial fibrillation than women. In the full multivariable model, the odds ratio (OR) of atrial fibrillation for each decade of advancing age was 2.1 for men and 2.2 for women (P < .0001). In addition, after multivariable adjustment, diabetes (OR, 1.4 for men and 1.6 for women), hypertension (OR, 1.5 for men and 1.4 for women), congestive heart failure (OR, 4.5 for men and 5.9 for women), and valve disease (OR, 1.8 for men and 3.4 for women) were significantly associated with risk for atrial fibrillation in both sexes. Myocardial infarction (OR, 1.4) was significantly associated with the development of atrial fibrillation in men. Women were significantly more likely than men to have valvular heart disease as a risk factor for atrial fibrillation. The multivariable models were largely unchanged after eliminating subjects with valvular heart disease. In addition to intrinsic cardiac causes such as valve disease and congestive heart failure, risk factors for cardiovascular disease also predispose to atrial fibrillation. Modification of risk factors for cardiovascular disease may have the added benefit of diminishing the incidence of atrial fibrillation.
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            MetPA: a web-based metabolomics tool for pathway analysis and visualization.

            MetPA (Metabolomics Pathway Analysis) is a user-friendly, web-based tool dedicated to the analysis and visualization of metabolomic data within the biological context of metabolic pathways. MetPA combines several advanced pathway enrichment analysis procedures along with the analysis of pathway topological characteristics to help identify the most relevant metabolic pathways involved in a given metabolomic study. The results are presented in a Google-map style network visualization system that supports intuitive and interactive data exploration through point-and-click, dragging and lossless zooming. Additional features include a comprehensive compound library for metabolite name conversion, automatic generation of analysis report, as well as the implementation of various univariate statistical procedures that can be accessed when users click on any metabolite node on a pathway map. MetPA currently enables analysis and visualization of 874 metabolic pathways, covering 11 common model organisms. Freely available at http://metpa.metabolomics.ca.
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              Choline and betaine in health and disease.

              Per Ueland (2011)
              Choline is an essential nutrient, but is also formed by de novo synthesis. Choline and its derivatives serve as components of structural lipoproteins, blood and membrane lipids, and as a precursor of the neurotransmitter acetylcholine. Pre-and postnatal choline availability is important for neurodevelopment in rodents. Choline is oxidized to betaine that serves as an osmoregulator and is a substrate in the betaine-homocysteine methyltransferase reaction, which links choline and betaine to the folate-dependent one-carbon metabolism. Choline and betaine are important sources of one-carbon units, in particular, during folate deficiency. Choline or betaine supplementation in humans reduces concentration of total homocysteine (tHcy), and plasma betaine is a strong predictor of plasma tHcy in individuals with low plasma concentration of folate and other B vitamins (B₂, B₆, and B₁₂) in combination TT genotype of the methylenetetrahydrofolate reductase 677 C->T polymorphism. The link to one-carbon metabolism and the recent availability of food composition data have motivated studies on choline and betaine as risk factors of chronic diseases previously studied in relation to folate and homocysteine status. High intake and plasma level of choline in the mother seems to afford reduced risk of neural tube defects. Intake of choline and betaine shows no consistent relation to cancer or cardiovascular risk or risk factors, whereas an unfavorable cardiovascular risk factor profile was associated with high choline and low betaine concentrations in plasma. Thus, choline and betaine showed opposite relations with key components of metabolic syndrome, suggesting a disruption of mitochondrial choline oxidation to betaine as part of the mitochondrial dysfunction in metabolic syndrome.
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                Author and article information

                Contributors
                Liujichun999@163.com
                zhangjingfw@163.com
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                16 November 2018
                16 November 2018
                2018
                : 8
                : 16930
                Affiliations
                [1 ]ISNI 0000 0004 1758 4073, GRID grid.412604.5, Cardiothoracic Surgery Department, , The First Affiliated Hospital of Nanchang University, ; Nanchang, 330006 China
                [2 ]ISNI 0000 0000 9889 6335, GRID grid.413106.1, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, , Chinese Academy of Medical Sciences and Peking Union Medical College, ; Beijing, 100037 China
                [3 ]GRID grid.452826.f, Yunnan Cancer Hospital, , The Third Affiliated Hospital of Kunming Medical University, ; Yunnan, 650118 China
                [4 ]ISNI 0000 0000 9889 6335, GRID grid.413106.1, Cardiovascular Surgery Department, Fuwai Hospital, , Chinese Academy of Medical Sciences and Peking Union Medical College, ; Beijing, 100037 China
                Article
                34930
                10.1038/s41598-018-34930-6
                6240090
                30446671
                e50c99dc-3646-4e7f-87a0-69f4807cce52
                © The Author(s) 2018

                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
                : 15 December 2017
                : 18 October 2018
                Funding
                Funded by: The current study was sponsored by the CAMS Innovation Fund for Medical Sciences [CIFMS, 2016-I2M-1-015] and the National Natural Science Foundation of China [Grant No. 81670376] and was also supported by the PUMC Youth Fund[Grant No. 2015-F10] and the Fundamental Research Funds for the Central Universities.
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