Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

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Record number 561606
Title Differential Network Analysis Reveals Metabolic Determinants Associated with Mortality in Acute Myocardial Infarction Patients and Suggests Potential Mechanisms Underlying Different Clinical Scores Used To Predict Death
Author(s) Vignoli, Alessia; Tenori, Leonardo; Giusti, Betti; Valente, Serafina; Carrabba, Nazario; Balzi, Daniela; Barchielli, Alessandro; Marchionni, Niccolò; Gensini, Gian Franco; Marcucci, Rossella; Gori, Anna Maria; Luchinat, Claudio; Saccenti, Edoardo
Source Journal of Proteome Research 19 (2020)2. - ISSN 1535-3893 - p. 949 - 961.
Department(s) Systems and Synthetic Biology
Publication type Refereed Article in a scientific journal
Publication year 2020
Keyword(s) acute myocardial infarction - metabolite−metabolite association networks - metabolomics - network inference - nuclear magnetic resonance

We present here the differential analysis of metabolite-metabolite association networks constructed from an array of 24 serum metabolites identified and quantified via nuclear magnetic resonance spectroscopy in a cohort of 825 patients of which 123 died within 2 years from acute myocardial infarction (AMI). We investigated differences in metabolite connectivity of patients who survived, at 2 years, the AMI event, and we characterized metabolite-metabolite association networks specific to high and low risks of death according to four different risk parameters, namely, acute coronary syndrome classification, Killip, Global Registry of Acute Coronary Events risk score, and metabolomics NOESY RF risk score. We show significant differences in the connectivity patterns of several low-molecular-weight molecules, implying variations in the regulation of several metabolic pathways regarding branched-chain amino acids, alanine, creatinine, mannose, ketone bodies, and energetic metabolism. Our results demonstrate that the characterization of metabolite-metabolite association networks is a promising and powerful tool to investigate AMI patients according to their outcomes at a molecular level.

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