Summary
The coronavirus disease-19 (COVID-19) pandemic has ravaged global healthcare with previously unseen levels of morbidity and mortality. In this study, we performed large-scale integrative multi-omics analyses of serum obtained from COVID-19 patients with the goal of uncovering novel pathogenic complexities of this disease and identifying molecular signatures that predict clinical outcomes. We assembled a network of protein-metabolite interactions through targeted metabolomic and proteomic profiling in 330 COVID-19 patients compared to 97 non-COVID, hospitalized controls. Our network identified distinct protein-metabolite cross talk related to immune modulation, energy and nucleotide metabolism, vascular homeostasis, and collagen catabolism. Additionally, our data linked multiple proteins and metabolites to clinical indices associated with long-term mortality and morbidity. Finally, we developed a novel composite outcome measure for COVID-19 disease severity based on metabolomics data. The model predicts severe disease with a concordance index of around 0.69, and shows high predictive power of 0.83–0.93 in two independent datasets.
【저자키워드】 Medicine, Physiology, Biological sciences, Clinical finding, human metabolism, 【초록키워드】 COVID-19, metabolomics, pandemic, Hospitalized, Mortality, metabolism, immune modulation, clinical outcomes, Protein, serum, Concordance, Coronavirus disease-19, morbidity, morbidity and mortality, collagen, molecular, disease, predict, metabolite, proteomic, nucleotide, Interaction, severe disease, COVID-19 patient, outcome measure, COVID-19 disease severity, independent datasets, pathogenic, predictive power, global healthcare, vascular homeostasis, controls, metabolomic, performed, analysis, cross talk, 【제목키워드】 severe COVID-19, Clinical outcome, proteomic, metabolomic,