For the study, which has been published in Lancet eBioMedicine journal, blood samples have been collected from healthcare workers who have contracted COVID and later, they were compared with blood samples of healthcare workers who were not infected.
Usually, protein levels in the body are stable. But there is a dramatic difference in levels of some of the proteins up to six weeks following infection. This suggests disruption to a number of important biological processes.
Later the researchers usined artificial intelligence to find 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 study's lead author Dr Gaby Captur (MRC Unit for Lifelong Health and Ageing at UCL) said, "Our study shows that even mild or asymptomatic Covid-19 disrupts the profile of proteins in our blood plasma. This means that even mild Covid-19 affects normal biological processes in a dramatic way, up to at least six weeks after infection.
"Our tool predicting long Covid still needs to be validated in an independent, larger group of patients. However, using our approach, a test that predicts long Covid at the time of initial infection could be rolled out quickly and in a cost-effective way.
"The method of analysis we used is readily available in hospitals and is high-throughput, meaning it can analyse thousands of samples in an afternoon."
Senior author Dr Wendy Heywood (UCL Great Ormond Street Institute of Child Health and Great Ormond Street Hospital) said, "If we can identify people who are likely to develop long Covid, this opens the door to trialling treatments such as anti-virals at this earlier, initial infection stage, to see if it can reduce the risk of later long Covid."
For 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.
(With inputs from agencies)