Blood test could predict the likelihood to get long COVID - Video

A blood test taken at the time of Covid-19 infection could predict who is most likely to develop long Covid, suggests a new small-scale study led by UCL researchers.

The study, published in Lancet eBioMedicine, analysed proteins in the blood of healthcare workers infected with SARS-CoV-2, comparing them to samples from healthcare workers who had not been infected.

Usually protein levels in the body are stable, but the researchers found a dramatic difference in levels of some of the proteins up to six weeks following infection, suggesting disruption to a number of important biological processes.

Using an artificial intelligence (AI) algorithm, they identified a "signature" in the abundance of different proteins that successfully predicted whether or not the person would go on to report persistent symptoms a year after infection.

The researchers say that, if these findings are repeated in a larger, independent group of patients, a test could potentially be offered alongside a polymerase chain reaction (PCR) test that could predict people's likelihood of developing long Covid.

3For the study, researchers analysed blood plasma samples from 54 healthcare workers who had PCR- or antibody-confirmed infection, taken every week for six weeks in spring 2020, comparing them to samples taken over the same period from 102 healthcare workers who were not infected.The researchers found abnormally high levels of 12 proteins out of the 91 studied among those infected by SARS-CoV-2, and that the degree of abnormality tracked with the severity of symptoms.

The research team found that at the time of first infection, abnormal levels of 20 proteins studied were predictive of persistent symptoms after one year. Most of these proteins were linked to anti-coagulant (anti-clotting) and anti-inflammatory processes.

Reference:

Dr Gaby Captur et al,Plasma proteomic signature predicts who will get persistent symptoms following SARS-CoV-2 infection,EBioMedicine,DOI: 10.1016/j.ebiom.2022.104293