São Paulo scientists find way to predict severity of COVID-19

A study was published by the São Carlos Chemistry Institute

Published in 22/06/2022 - 06:17 By Camila Boehm - São Paulo

Scientists at the São Carlos Chemistry Institute (IQSC) of the University of São Paulo (USP) have identified a way to potentially predict the severity of COVID-19 infections through blood plasma analysis. The method can serve as a tool for care providers to screen patients and to prevent their state from deteriorating. An article was published in Journal of Proteome Research.

In the study, those infected with the disease showed variations in the concentration of six substances found in the blood called metabolites—glycerol, acetate, 3-aminoisobutyrate, formate, glucuronate, and lactate. It was found that the greater the imbalance in the amount of these substances at the beginning of the infection, the more severe patients’ health conditions were.


The analysis was performed on blood plasma samples from 110 patients with flu symptoms who were admitted at the Hospital of the Federal University of São Paulo (UNIFESP) in 2020—57 of whom not infected with COVID-19, and the other 53 recent positive cases of the disease.

The researchers observed that ten of the infected patients faced complications and were admitted to the ICU, with two deaths. This group, which was more severe, presented more significant variations in the concentration of the mentioned metabolites early in their COVID-19 infection period.

The results may contribute to the creation of a new clinical protocol that could help doctors and hospitals identify patients who may develop a severe form of the disease in the first days of symptoms, allowing them to intervene and prevent disease progression.

To validate the technique, the institute went on to declare, the scientists plan to expand the number of plasma samples evaluated and to incorporate new groups into the next steps of the study, such as those who have been vaccinated and contracted COVID-19. Also to be included are gender and age data.

Translation: Fabrício Ferreira -  Edition: Kleber Sampaio

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