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      Association between proteomic biomarkers and myocardial fibrosis measured by MRI: the multi-ethnic study of atherosclerosis

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          Summary

          Background

          Cardiac magnetic resonance imaging (CMR) determines the extent of interstitial fibrosis, measured by increased extracellular volume (ECV), and replacement fibrosis with late gadolinium myocardial enhancement (LGE). Despite advances in detection, the pathophysiology of subclinical myocardial fibrosis is incompletely understood. Targeted proteomic discovery technologies enable quantification of low abundance circulating proteins to elucidate cardiac fibrosis mechanisms.

          Methods

          Using a cross-sectional design, we selected 92 LGE+ cases and 92 LGE− demographically matched controls from the Multi-Ethnic Study of Atherosclerosis. Similarly, we selected 156 cases from the highest ECV quartile and matched with 156 cases from the lowest quartile. The plasma serum proteome was analyzed using proximity extension assays to determine differential regulation of 92 proteins previously implicated with cardiovascular disease. Results were analyzed using volcano plots of statistical significance vs. magnitude of change and Bayesian additive regression tree (BART) models to determine importance.

          Findings

          After adjusting for false discovery, higher ECV was significantly associated with 17 proteins. Using BART, Plasminogen activator inhibitor 1, Insulin-like growth factor-binding protein 1, and N-terminal pro-B-type natriuretic peptide were associated with higher ECV after accounting for other proteins and traditional cardiovascular risk factors. In contrast, no circulating proteins were associated with replacement fibrosis.

          Interpretations

          Our results suggest unique circulating proteomic signatures associated with interstitial fibrosis emphasizing its systemic influences. With future validation, protein panels may identify patients who may develop interstitial fibrosis with progression to heart failure.

          Funding

          This research was supported by contracts and grants from doi 10.13039/100000050, NHLBI; , doi 10.13039/100006180, NCATS; and the Inova Heart and Vascular Institute.

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          Most cited references57

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          Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

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            WGCNA: an R package for weighted correlation network analysis

            Background Correlation networks are increasingly being used in bioinformatics applications. For example, weighted gene co-expression network analysis is a systems biology method for describing the correlation patterns among genes across microarray samples. Weighted correlation network analysis (WGCNA) can be used for finding clusters (modules) of highly correlated genes, for summarizing such clusters using the module eigengene or an intramodular hub gene, for relating modules to one another and to external sample traits (using eigengene network methodology), and for calculating module membership measures. Correlation networks facilitate network based gene screening methods that can be used to identify candidate biomarkers or therapeutic targets. These methods have been successfully applied in various biological contexts, e.g. cancer, mouse genetics, yeast genetics, and analysis of brain imaging data. While parts of the correlation network methodology have been described in separate publications, there is a need to provide a user-friendly, comprehensive, and consistent software implementation and an accompanying tutorial. Results The WGCNA R software package is a comprehensive collection of R functions for performing various aspects of weighted correlation network analysis. The package includes functions for network construction, module detection, gene selection, calculations of topological properties, data simulation, visualization, and interfacing with external software. Along with the R package we also present R software tutorials. While the methods development was motivated by gene expression data, the underlying data mining approach can be applied to a variety of different settings. Conclusion The WGCNA package provides R functions for weighted correlation network analysis, e.g. co-expression network analysis of gene expression data. The R package along with its source code and additional material are freely available at .
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              Multi-ethnic study of atherosclerosis: objectives and design.

              D E Bild (2002)
              The Multi-Ethnic Study of Atherosclerosis was initiated in July 2000 to investigate the prevalence, correlates, and progression of subclinical cardiovascular disease (CVD) in a population-based sample of 6,500 men and women aged 45-84 years. The cohort will be selected from six US field centers. Approximately 38% of the cohort will be White, 28% African-American, 23% Hispanic, and 11% Asian (of Chinese descent). Baseline measurements will include measurement of coronary calcium using computed tomography; measurement of ventricular mass and function using cardiac magnetic resonance imaging; measurement of flow-mediated brachial artery endothelial vasodilation, carotid intimal-medial wall thickness, and distensibility of the carotid arteries using ultrasonography; measurement of peripheral vascular disease using ankle and brachial blood pressures; electrocardiography; and assessments of microalbuminuria, standard CVD risk factors, sociodemographic factors, life habits, and psychosocial factors. Blood samples will be assayed for putative biochemical risk factors and stored for use in nested case-control studies. DNA will be extracted and lymphocytes will be immortalized for genetic studies. Measurement of selected subclinical disease indicators and risk factors will be repeated for the study of progression over 7 years. Participants will be followed through 2008 for identification and characterization of CVD events, including acute myocardial infarction and other coronary heart disease, stroke, peripheral vascular disease, and congestive heart failure; therapeutic interventions for CVD; and mortality.
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                Author and article information

                Contributors
                Journal
                eBioMedicine
                EBioMedicine
                eBioMedicine
                Elsevier
                2352-3964
                28 February 2023
                April 2023
                28 February 2023
                : 90
                : 104490
                Affiliations
                [a ]Division of Cardiology, Johns Hopkins University, Baltimore, MD, USA
                [b ]Georgetown University School of Medicine, Washington, DC, USA
                [c ]Department of Statistics, Texas A&M University, College Station, TX, USA
                [d ]Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
                [e ]Geriatric Research and Education Clinical Center, VA Maryland Healthcare System, Baltimore, MD, USA
                [f ]Inova Heart and Vascular Institute, Falls Church, VA, USA
                [g ]Department of Radiology, Johns Hopkins Hospital, Baltimore, MD, USA
                [h ]Department of Statistics, George Mason University, Fairfax, VA, USA
                Author notes
                []Corresponding author. Inova Heart and Vascular Institute, 3300 Gallows Rd, Falls Church, 22042, VA, USA. Christopher.Defilippi@ 123456Inova.org
                Article
                S2352-3964(23)00055-5 104490
                10.1016/j.ebiom.2023.104490
                10006438
                36857966
                a8b4781a-4968-47dd-a45a-65206b7542af
                © 2023 The Author(s)

                This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

                History
                : 6 November 2022
                : 7 February 2023
                : 8 February 2023
                Categories
                Articles

                myocardial fibrosis,interstitial fibrosis,replacement fibrosis,proteomic,biomarkers,cardiac mri

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