Abstract:
The long-term symptoms of COVID-19 are the subject of public and scientific discussions. Understanding how those long COVID symptoms co-occur in clusters of syndromes may indicate the pathogenic mechanisms of long COVID. Our study objective was to cluster the different long COVID symptoms. We included persons who had a COVID-19 and assessed long-term symptoms (at least 4 weeks after first symptoms). Hierarchical clustering was applied to the symptoms as well as to the participants based on the Euclidean distance h of the log-values of the answers on symptom severity. The distribution of clusters within our cohort is shown in a heat map.
From September 2021 to November 2023, 2371 persons with persisting long COVID symptoms participated in the study. Self-assessed long COVID symptoms were assigned to three symptom clusters. Cluster A unites rheumatological and neurological symptoms, cluster B includes neuro-psychological symptoms together with cardiorespiratory symptoms, and a third cluster C shows an association of general infection signs, dermatological and otology symptoms. A high proportion of the participants (n = 1424) showed symptoms of all three clusters.
Clustering of long COVID symptoms reveals similarities to the symptomatology of already described syndromes such as the Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) or rheumatological autoinflammatory diseases. Further research may identify serological parameters or clinical risk factors associated with the shown clusters and might improve our understanding of long COVID as a systemic disease. Furthermore, multimodal treatments can be developed and scaled for symptom clusters and associated impairments.
Source: Niewolik J, Mikuteit M, Klawitter S, Schröder D, Stölting A, Vahldiek K, Heinemann S, Müller F, Behrens G, Klawonn F, Dopfer-Jablonka A, Steffens S. Cluster analysis of long COVID symptoms for deciphering a syndrome and its long-term consequence. Immunol Res. 2024 Apr 16. doi: 10.1007/s12026-024-09465-w. Epub ahead of print. PMID: 38627327. https://link.springer.com/article/10.1007/s12026-024-09465-w (Full text)