BMJ open respiratory research, Volume 11, Issue 1, 24 4 2024, Pages e001907 Long COVID exhibits clinically distinct phenotypes at 3-6 months post-SARS-CoV-2 infection: results from the P4O2 consortium. Blankestijn JM, Abdel-Aziz MI, Baalbaki N, Bazdar S, Beekers I, Beijers RJHCG, Bloemsma LD, Cornelissen MEB, Gach D, Houweling L, Holverda S, Jacobs JJL, Jonker R, van der Lee I, Linders PMA, Mohamed Hoesein FAA, Noij LCE, Nossent EJ, van de Pol MA, Schaminee DW, Schols AMWJ, Schuurman LT, Sondermeijer B, Geelhoed JJM, van den Bergh JP, Weersink EJM, de Wit-van Wijck Y, Maitland-van der Zee AH, P4O2 consortium
Background
Four months after SARS-CoV-2 infection, 22%-50% of COVID-19 patients still experience complaints. Long COVID is a heterogeneous disease and finding subtypes could aid in optimising and developing treatment for the individual patient.
Methods
Data were collected from 95 patients in the P4O2 COVID-19 cohort at 3-6 months after infection. Unsupervised hierarchical clustering was performed on patient characteristics, characteristics from acute SARS-CoV-2 infection, long COVID symptom data, lung function and questionnaires describing the impact and severity of long COVID. To assess robustness, partitioning around medoids was used as alternative clustering.
Results
Three distinct clusters of patients with long COVID were revealed. Cluster 1 (44%) represented predominantly female patients (93%) with pre-existing asthma and suffered from a median of four symptom categories, including fatigue and respiratory and neurological symptoms. They showed a milder SARS-CoV-2 infection. Cluster 2 (38%) consisted of predominantly male patients (83%) with cardiovascular disease (CVD) and suffered from a median of three symptom categories, most commonly respiratory and neurological symptoms. This cluster also showed a significantly lower forced expiratory volume within 1 s and diffusion capacity of the lung for carbon monoxide. Cluster 3 (18%) was predominantly male (88%) with pre-existing CVD and diabetes. This cluster showed the mildest long COVID, and suffered from symptoms in a median of one symptom category.
Conclusions
Long COVID patients can be clustered into three distinct phenotypes based on their clinical presentation and easily obtainable information. These clusters show distinction in patient characteristics, lung function, long COVID severity and acute SARS-CoV-2 infection severity. This clustering can help in selecting the most beneficial monitoring and/or treatment strategies for patients suffering from long COVID. Follow-up research is needed to reveal the underlying molecular mechanisms implicated in the different phenotypes and determine the efficacy of treatment.
