Plasma metabolomics profiling identifies new predictive biomarkers for disease severity in COVID-19 patients

Nelson C Soares, Amal Hussein, Jibran Sualeh Muhammad, Mohammad H Semreen, Gehad ElGhazali and Mawieh Hamad

PLOS ONE, 2023, vol. 18, issue 8, 1-20

Abstract: Recently, numerous studies have reported on different predictive models of disease severity in COVID-19 patients. Herein, we propose a highly predictive model of disease severity by integrating routine laboratory findings and plasma metabolites including cytosine as a potential biomarker of COVID-19 disease severity. One model was developed and internally validated on the basis of ROC-AUC values. The predictive accuracy of the model was 0.996 (95% CI: 0.989 to 1.000) with an optimal cut-off risk score of 3 from among 6 biomarkers including five lab findings (D-dimer, ferritin, neutrophil counts, Hp, and sTfR) and one metabolite (cytosine). The model is of high predictive power, needs a small number of variables that can be acquired at minimal cost and effort, and can be applied independent of non-empirical clinical data. The metabolomics profiling data and the modeling work stemming from it, as presented here, could further explain the cause of COVID-19 disease prognosis and patient management.

Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0289738

DOI: 10.1371/journal.pone.0289738

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