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

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

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.

Suggested Citation

  • Nelson C Soares & Amal Hussein & Jibran Sualeh Muhammad & Mohammad H Semreen & Gehad ElGhazali & Mawieh Hamad, 2023. "Plasma metabolomics profiling identifies new predictive biomarkers for disease severity in COVID-19 patients," PLOS ONE, Public Library of Science, vol. 18(8), pages 1-20, August.
    • Handle: RePEc:plo:pone00:0289738
    DOI: 10.1371/journal.pone.0289738
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