Three types of long-COVID for people with symptoms for 12 weeks or more

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The preprint, published today on MedrXiv, also identifies patterns among people infected with wild type, the first strain of SARS-CoV-2, and Delta and Alpha variants, among vaccinated and unvaccinated individuals.

King’s researchers analyzed data from 1,459 people who experienced continuous symptoms for more than 84 days, known clinically as post-covid syndrome or long COVID, from the ZOE Health study.

The analysis adds to emerging evidence that long COVID is not a homogeneous disease and should have individualized treatment and care. The team recommends that the results be used by researchers studying why and how long COVID occurs, health service providers, as well as people living with long COVID.

People with symptoms for 12 weeks or more fell into three main groups based on the types of symptoms they were experiencing. The largest cluster was characterized by a cluster of neurological symptoms such as fatigue, brain fog, and headache and was the most common subtype among alpha and delta variants. A second group experienced respiratory symptoms, including chest pain and severe shortness of breath, which may indicate lung damage. This was the largest group in the wild-type period when the population was unvaccinated. Finally, some people have experienced a wide range of symptoms, including heart palpitations, muscle aches, and changes in skin and hair.

Importantly, the data also suggests that the patterns of symptoms for people who experienced symptoms for 12 weeks or more were similar in vaccinated and unvaccinated people, at least with variants that had this data. Existing data shows that the overall risk of long-COVID is reduced by vaccination.

While these three subtypes were evident across all variants, additional symptom clusters were also identified that were slightly different between variants. These differences may not be due to the variants themselves, but to other factors that have changed during the pandemic, such as time of year, social behaviors and treatments.

Dr Marc Modat, from the School of Biomedical Engineering & Imaging Sciences, who led the analysis, said: “Machine learning approaches, such as clustering analysis, have started to explore and identify different post-COVID syndrome profiles. This opens new avenues of research to better understand COVID-19 and motivate clinical research that could mitigate the long-term effects of the disease.

She added: “Given the time series component, our study is relevant to post-COVID prognosis, indicating how long certain symptoms may last. This knowledge could help in the development of personalized diagnosis and treatment for these people.


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