TY - JOUR
T1 - Long COVID exhibits clinically distinct phenotypes at 3–6 months post-SARSCoV-2 infection
T2 - results from the P4O2 consortium
AU - Blankestijn, Jelle M.
AU - Abdel-Aziz, Mahmoud I.
AU - Baalbaki, Nadia
AU - Bazdar, Somayeh
AU - Beekers, Inés
AU - Beijers, Rosanne J. H. C. G.
AU - Bloemsma, Lizan D.
AU - Cornelissen, Merel E. B.
AU - Gach, Debbie
AU - Houweling, Laura
AU - Holverda, Sebastiaan
AU - Jacobs, John J. L.
AU - Jonker, Reneé
AU - van der Lee, Ivo
AU - Linders, Paulien M. A.
AU - Mohamed Hoesein, Firdaus A. A.
AU - Noij, Lieke C. E.
AU - Nossent, Esther J.
AU - van de Pol, Marianne A.
AU - Schaminee, Daphne W.
AU - Schols, Annemie M. W. J.
AU - Schuurman, Lisanne T.
AU - Sondermeijer, Brigitte
AU - Geelhoed, J. J. Miranda
AU - van den Bergh, Joop P.
AU - Weersink, Els J. M.
AU - de Wit-van Wijck, Yolanda
AU - on behalf of the P4O2 Consortium
AU - Maitland-van der Zee, Anke H.
N1 - Publisher Copyright:
© Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.
PY - 2024/4/24
Y1 - 2024/4/24
N2 - 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 SARSCoV-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.
AB - 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 SARSCoV-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.
UR - https://www.scopus.com/pages/publications/85191418982
U2 - 10.1136/bmjresp-2023-001907
DO - 10.1136/bmjresp-2023-001907
M3 - Article
C2 - 38663887
SN - 2052-4439
VL - 11
JO - BMJ Open Respiratory Research
JF - BMJ Open Respiratory Research
IS - 1
M1 - e001907
ER -
SDG 3 Good Health and Well-being