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      Inflammation and remodeling pathways and risk of cardiovascular events in patients with ischemic heart failure and reduced ejection fraction

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          Abstract

          Patients with heart failure (HF) and coronary artery disease (CAD) have a high risk for cardiovascular (CV) events including HF hospitalization, stroke, myocardial infarction (MI) and sudden cardiac death (SCD). The present study evaluated associations of proteomic biomarkers with CV outcome in patients with CAD and HF with reduced ejection fraction (HFrEF), shortly after a worsening HF episode. We performed a case–control study within the COMMANDER HF international, double-blind, randomized placebo-controlled trial investigating the effects of the factor-Xa inhibitor rivaroxaban. Patients with the following first clinical events: HF hospitalization, SCD and the composite of MI or stroke were matched with corresponding controls for age, sex and study drug. Plasma concentrations of 276 proteins with known associations with CV and cardiometabolic mechanisms were analyzed. Results were corrected for multiple testing using false discovery rate (FDR). In 485 cases and 455 controls, 49 proteins were significantly associated with clinical events of which seven had an adjusted FDR < 0.001 (NT-proBNP, BNP, T-cell immunoglobulin and mucin domain containing 4 (TIMD4), fibroblast growth factor 23 (FGF-23), growth differentiation factor-15 (GDF-15), pulmonary surfactant-associated protein D (PSP-D) and Spondin-1 (SPON1)). No significant interactions were identified between the type of clinical event (MI/stroke, SCD or HFH) and specific biomarkers (all interaction FDR > 0.20). When adding the biomarkers significantly associated with the above outcome to a clinical model (including NT-proBNP), the C-index increase was 0.057 (0.033–0.082), p < 0.0001 and the net reclassification index was 54.9 (42.5 to 67.3), p < 0.0001. In patients with HFrEF and CAD following HF hospitalization, we found that NT-proBNP, BNP, TIMD4, FGF-23, GDF-15, PSP-D and SPON1, biomarkers broadly associated with inflammation and remodeling mechanistic pathways, were strong but indiscriminate predictors of a variety of individual CV events.

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          Most cited references31

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          Cytoscape: a software environment for integrated models of biomolecular interaction networks.

          Cytoscape is an open source software project for integrating biomolecular interaction networks with high-throughput expression data and other molecular states into a unified conceptual framework. Although applicable to any system of molecular components and interactions, Cytoscape is most powerful when used in conjunction with large databases of protein-protein, protein-DNA, and genetic interactions that are increasingly available for humans and model organisms. Cytoscape's software Core provides basic functionality to layout and query the network; to visually integrate the network with expression profiles, phenotypes, and other molecular states; and to link the network to databases of functional annotations. The Core is extensible through a straightforward plug-in architecture, allowing rapid development of additional computational analyses and features. Several case studies of Cytoscape plug-ins are surveyed, including a search for interaction pathways correlating with changes in gene expression, a study of protein complexes involved in cellular recovery to DNA damage, inference of a combined physical/functional interaction network for Halobacterium, and an interface to detailed stochastic/kinetic gene regulatory models.
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            Moving towards best practice when using inverse probability of treatment weighting (IPTW) using the propensity score to estimate causal treatment effects in observational studies

            The propensity score is defined as a subject's probability of treatment selection, conditional on observed baseline covariates. Weighting subjects by the inverse probability of treatment received creates a synthetic sample in which treatment assignment is independent of measured baseline covariates. Inverse probability of treatment weighting (IPTW) using the propensity score allows one to obtain unbiased estimates of average treatment effects. However, these estimates are only valid if there are no residual systematic differences in observed baseline characteristics between treated and control subjects in the sample weighted by the estimated inverse probability of treatment. We report on a systematic literature review, in which we found that the use of IPTW has increased rapidly in recent years, but that in the most recent year, a majority of studies did not formally examine whether weighting balanced measured covariates between treatment groups. We then proceed to describe a suite of quantitative and qualitative methods that allow one to assess whether measured baseline covariates are balanced between treatment groups in the weighted sample. The quantitative methods use the weighted standardized difference to compare means, prevalences, higher‐order moments, and interactions. The qualitative methods employ graphical methods to compare the distribution of continuous baseline covariates between treated and control subjects in the weighted sample. Finally, we illustrate the application of these methods in an empirical case study. We propose a formal set of balance diagnostics that contribute towards an evolving concept of ‘best practice’ when using IPTW to estimate causal treatment effects using observational data. © 2015 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.
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              The prognostic value of B-type natriuretic peptide in patients with acute coronary syndromes.

              Brain (B-type) natriuretic peptide is a neurohormone synthesized predominantly in ventricular myocardium. Although the circulating level of this neurohormone has been shown to provide independent prognostic information in patients with transmural myocardial infarction, few data are available for patients with acute coronary syndromes in the absence of ST-segment elevation. We measured B-type natriuretic peptide in plasma specimens obtained a mean (+/-SD) of 40+/-20 hours after the onset of ischemic symptoms in 2525 patients from the Orbofiban in Patients with Unstable Coronary Syndromes-Thrombolysis in Myocardial Infarction 16 study. The base-line level of B-type natriuretic peptide was correlated with the risk of death, heart failure, and myocardial infarction at 30 days and 10 months. The unadjusted rate of death increased in a stepwise fashion among patients in increasing quartiles of base-line B-type natriuretic peptide levels (P< 0.001). This association remained significant in subgroups of patients who had myocardial infarction with ST-segment elevation (P=0.02), patients who had myocardial infarction without ST-segment elevation (P<0.001), and patients who had unstable angina (P<0.001). After adjustment for independent predictors of the long-term risk of death, the odds ratios for death at 10 months in the second, third, and fourth quartiles of B-type natriuretic peptide were 3.8 (95 percent confidence interval, 1.1 to 13.3), 4.0 (95 percent confidence interval, 1.2 to 13.7), and 5.8 (95 percent confidence interval, 1.7 to 19.7). The level of B-type natriuretic peptide was also associated with the risk of new or recurrent myocardial infarction (P=0.01) and new or worsening heart failure (P<0.001) at 10 months. A single measurement of B-type natriuretic peptide, obtained in the first few days after the onset of ischemic symptoms, provides powerful information for use in risk stratification across the spectrum of acute coronary syndromes. This finding suggests that cardiac neurohormonal activation may be a unifying feature among patients at high risk for death after acute coronary syndromes.
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                Author and article information

                Contributors
                f.zannad@chru-nancy.fr
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                20 May 2022
                20 May 2022
                2022
                : 12
                : 8574
                Affiliations
                [1 ]GRID grid.29172.3f, ISNI 0000 0001 2194 6418, Université de Lorraine, Centre d’Investigation Clinique-Plurithématique Inserm CIC-P 1433, Inserm U1116, CHRU Nancy Brabois, , F-CRIN INI-CRCT (Cardiovascular and Renal Clinical Trialists), ; Nancy, France
                [2 ]GRID grid.8756.c, ISNI 0000 0001 2193 314X, Robertson Centre for Biostatistics and Clinical Trials, , University of Glasgow, ; Glasgow, Scotland
                [3 ]GRID grid.6363.0, ISNI 0000 0001 2218 4662, Department of Cardiology (CVK), and Berlin Institute of Health Center for Regenerative Therapies (BCRT), German Centre for Cardiovascular Research (DZHK) Partner Site Berlin, , Charité Universitätsmedizin Berlin, ; Berlin, Germany
                [4 ]GRID grid.497530.c, ISNI 0000 0004 0389 4927, Janssen Research and Development, ; Raritan, NJ USA
                [5 ]GRID grid.428397.3, ISNI 0000 0004 0385 0924, National Heart Centre Singapore, , Duke-National University of Singapore, ; Singapore, Singapore
                [6 ]GRID grid.62560.37, ISNI 0000 0004 0378 8294, Brigham and Women’s Hospital and Harvard Medical School, ; Boston, MA USA
                [7 ]GRID grid.4830.f, ISNI 0000 0004 0407 1981, Department of Cardiology, University Medical Center Groningen, , University of Groningen, ; Groningen, The Netherlands
                [8 ]Cardiology Division, Department of Medicine, University of California, La Jolla, San Diego, USA
                Article
                12385
                10.1038/s41598-022-12385-0
                9123183
                35595781
                468c3d31-9869-4ac6-af7e-15d0bec68c45
                © 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 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
                : 26 August 2021
                : 21 March 2022
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                © The Author(s) 2022

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                cardiology,cardiovascular biology
                Uncategorized
                cardiology, cardiovascular biology

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