Post-acute sequelae of COVID-19 (PASC) are associated with distinct clinical phenotypes two years after SARS-CoV-2 infection: A data-driven approach

It is important to determine whether the persistent cognitive and fatigue complaints, as part of post-acute sequelae of COVID-19 (PASC), are associated with underlying patterns and specific symptoms clusters. Therefore, the aim of the present study was to investigate symptom clusters and clinical phenotypes in PASC. We included n = 153 individuals (53 years old, 66% female, 2 years post SARS-CoV-2 infection) categorized into groups with (n = 88) and without (n = 65) PASC, defined by severe fatigue and concentration problems using validated cut-offs. Symptom clusters (i.e., component loadings and component scores) were identified using principal component analyses (PCA, eigenvalue[EV]>1.0), using clinical, somatic, coping and sleep factors, symptom severity, neuropsychological and physical tests (totalling 19 tests and questionnaires). We then used logistic regression techniques on (participant-level) component scores (standardized) to investigate associations with PASC. Finally, K-means clustering methods to identify clinical phenotypes. Analyses were adjusted for age, sex, education, nationality and time since infection. PCA identified 5 components, explaining 70% of the variance. We labelled these components as follows: (1) PEM, ME/CFS and sleep symptoms, coping factors (EV = 7.0, 36.9% variance), (2) information-processing speed (EV = 2.5, 13.3%), (3) executive functioning (EV = 1.5, 8.0%, (4) physical performance (EV = 1.2, 6.5%), (5) somatic comorbidities and hospital admission (EV = 1.0, 5.5%). Logistic regression showed that only the first component scores (Odds ratio [95% confidence interval] = 97.1 [16.5-570.3], p < 0.001,), and none of the others (all p > 0.05), associated with PASC. Cluster analysis revealed two distinct clinical phenotypes among individuals with severe chronic fatigue: one marked by physical impairment and the other by diminished cognitive performance. Consistent with existing literature, we confirmed that PASC is a heterogeneous syndrome marked by extensive, interrelated symptoms. Notably, we identified specific cognitive and physical clinical phenotypes. These findings are important for future targeted diagnosis and treatment.

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PLoS ONE
Published
2026-10-15
DOI
https://doi.org/10.1371/journal.pone.0359844
Primary Topic
Long-Term Effects of COVID-19
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article
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article

Post-acute sequelae of COVID-19 (PASC) are associated with distinct clinical phenotypes two years after SARS-CoV-2 infection: A data-driven approach

Janneke Horn, Hans Knoop, Anouk Verveen, Johanna M. A. Visser‐Meily et al.
PLoS ONE
Long-Term Effects of COVID-19
article

Post-acute sequelae of COVID-19 (PASC) are associated with distinct clinical phenotypes two years after SARS-CoV-2 infection: A data-driven approach

Janneke Horn, Hans Knoop, Anouk Verveen, Johanna M. A. Visser‐Meily, Caroline M Van Heugten, Yvonne M. G. van Os, Denise Visser, Jos A. Bosch, Brent Appelman, Ronald Boellaard, Cees C. van den Wijngaard, Hanneke E. Hulst, Sander C.J. Verfaillie, Maria Prins, Bart N.M. van Berckel, Tanja A Kuut, Menno D. de Jong, Tessa van der Maaden, Pythia T. Nieuwkerk, Nelleke Tolboom, Michele van Vugt
article en

Abstract

It is important to determine whether the persistent cognitive and fatigue complaints, as part of post-acute sequelae of COVID-19 (PASC), are associated with underlying patterns and specific symptoms clusters. Therefore, the aim of the present study was to investigate symptom clusters and clinical phenotypes in PASC. We included n = 153 individuals (53 years old, 66% female, 2 years post SARS-CoV-2 infection) categorized into groups with (n = 88) and without (n = 65) PASC, defined by severe fatigue and concentration problems using validated cut-offs. Symptom clusters (i.e., component loadings and component scores) were identified using principal component analyses (PCA, eigenvalue[EV]>1.0), using clinical, somatic, coping and sleep factors, symptom severity, neuropsychological and physical tests (totalling 19 tests and questionnaires). We then used logistic regression techniques on (participant-level) component scores (standardized) to investigate associations with PASC. Finally, K-means clustering methods to identify clinical phenotypes. Analyses were adjusted for age, sex, education, nationality and time since infection. PCA identified 5 components, explaining 70% of the variance. We labelled these components as follows: (1) PEM, ME/CFS and sleep symptoms, coping factors (EV = 7.0, 36.9% variance), (2) information-processing speed (EV = 2.5, 13.3%), (3) executive functioning (EV = 1.5, 8.0%, (4) physical performance (EV = 1.2, 6.5%), (5) somatic comorbidities and hospital admission (EV = 1.0, 5.5%). Logistic regression showed that only the first component scores (Odds ratio [95% confidence interval] = 97.1 [16.5-570.3], p < 0.001,), and none of the others (all p > 0.05), associated with PASC. Cluster analysis revealed two distinct clinical phenotypes among individuals with severe chronic fatigue: one marked by physical impairment and the other by diminished cognitive performance. Consistent with existing literature, we confirmed that PASC is a heterogeneous syndrome marked by extensive, interrelated symptoms. Notably, we identified specific cognitive and physical clinical phenotypes. These findings are important for future targeted diagnosis and treatment.

PLoS ONEVol. 21(10)
Amsterdam UMC Location University of Amsterdam (NL), University Medical Center Utrecht (NL), Maastricht University (NL), National Institute for Public Health and the Environment (NL), Amsterdam Public Health (NL), GGZ inGeest (NL), GGD Amsterdam (NL), Amsterdam University Medical Centers (NL), Human Resources University (KH), Amsterdam UMC Location Vrije Universiteit Amsterdam (NL)
ZonMw
Good health and well-being
Openalex Percentile: Top 14%
Long-Term Effects of COVID-19
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