{
  "abstract": "Post-COVID syndrome (PCS) is characterised by persistent symptoms affecting multiple organ systems. Despite evidence of immune dysregulation, the protein signatures driving PCS remain unclear. This study applies stratified machine learning and enrichment analysis to identify protein markers distinguishing PCS groups from recovered individuals longitudinally. We analysed samples from 365 individuals who had been hospitalised for COVID-19, classifying them into four symptom-based groups (Fatigue, Affective, Cardiopulmonary, and Gastrointestinal) and a recovered group. Up to 384 inflammatory plasma proteins were measured at two research visits (5 and 12 months post-hospital discharge) using Olink Explore. Stratified, cross-validated penalised logistic regression models were trained and evaluated using AUC, accuracy, and F1-score. Enrichment analysis was performed to identify functionally related pathways. Overall, several biomarkers (CCL7, CTRC, FCAR, IL10RA, LAMP3, MVK, TPSAB1, LGALS9) were consistently upregulated across most groups, suggesting a shared systemic immune dysregulation mechanism. Per-group analysis revealed additional insights: Fatigue (HSD11B1, CKMT1A, MATN2), Gastrointestinal (CLEC4C, CKMT1A, CNTNAP2), and Affective (MATN2, ISM1). Additionally, enrichment analysis revealed that shared pathways across PCS symptom subgroups predominantly involve immune system regulation, including leukocyte activation, cytokine-mediated signalling, and response to cytokines. These findings suggest a common underlying axis of persistent immune dysregulation contributing to symptom manifestation across domains. Our findings suggest that all PCS groups share immune and vascular dysregulation as a core mechanism, with some symptom-specific proteins identified for each group.",
  "authors": [
    {
      "affiliations": [
        "Institute for Lung Health, Leicester NIHR Biomedical Research Centre, University of Leicester, Leicester, UK"
      ],
      "name": "K Ntotsis"
    },
    {
      "affiliations": [
        "National Heart and Lung Institute, Imperial College London, London, UK"
      ],
      "name": "C Efstathiou"
    },
    {
      "affiliations": [
        "Institute for Lung Health, Leicester NIHR Biomedical Research Centre, University of Leicester, Leicester, UK"
      ],
      "name": "M Richardson"
    },
    {
      "affiliations": [
        "National Heart and Lung Institute, Imperial College London, London, UK"
      ],
      "name": "RS Thwaites"
    },
    {
      "affiliations": [
        "National Heart and Lung Institute, Imperial College London, London, UK"
      ],
      "name": "F Liew"
    },
    {
      "affiliations": [
        "Institute for Lung Health, Leicester NIHR Biomedical Research Centre, University of Leicester, Leicester, UK"
      ],
      "name": "D Lozano-Rojas"
    },
    {
      "affiliations": [
        "Institute for Lung Health, Leicester NIHR Biomedical Research Centre, University of Leicester, Leicester, UK"
      ],
      "name": "M Sereno"
    },
    {
      "affiliations": [
        "Institute for Lung Health, Leicester NIHR Biomedical Research Centre, University of Leicester, Leicester, UK"
      ],
      "name": "RA Evans"
    },
    {
      "affiliations": [
        "Institute for Lung Health, Leicester NIHR Biomedical Research Centre, University of Leicester, Leicester, UK",
        "Department of Population Health Sciences, University of Leicester, Leicester, UK"
      ],
      "name": "LV Wain"
    },
    {
      "affiliations": [
        "National Heart and Lung Institute, Imperial College London, London, UK"
      ],
      "name": "PJM Openshaw"
    },
    {
      "affiliations": [
        "Institute for Lung Health, Leicester NIHR Biomedical Research Centre, University of Leicester, Leicester, UK"
      ],
      "name": "CE Brightling"
    }
  ],
  "title": "S56 Machine learning and enrichment analysis reveal persistent protein signatures in longitudinal post-COVID symptom trajectories",
  "uid": "8a91df65-fefa-5278-b89a-8d135f17e43a"
}