Sequential multi-omics analysis identifies clinical phenotypes and predictive biomarkers for long COVID.

Area of Science:

• Immunology
• Metabolomics
• Systems Biology

Background:

• Post-acute sequelae of COVID-19 (PASC), or long COVID, presents with diverse, debilitating multisystem symptoms.
• Understanding the biological underpinnings of long COVID during convalescence is crucial for effective treatment strategies.

Purpose of the Study:

• To comprehensively profile molecular changes in plasma during and after acute COVID-19 infection.
• To identify biological processes and biomarkers associated with long COVID development and adverse outcomes.

Main Methods:

• Collected plasma samples from 117 individuals during acute infection and 6 months post-infection.
• Utilized multi-omics approaches (cytokines, proteome, metabolome) and network analysis.
• Developed a prognostic model using machine learning to predict clinical outcomes.

Main Results:

• Identified sustained inflammation, platelet degranulation, and cellular activation during convalescence.
• Revealed dysregulation in key metabolic pathways including arginine biosynthesis, methionine, taurine metabolism, and the TCA cycle.
• Developed a 20-molecule prognostic model predicting adverse outcomes with 83% accuracy (AUC 0.96).

Conclusions:

• Convalescence from COVID-19 involves distinct biological processes compared to acute infection.
• Sustained inflammatory and metabolic alterations are implicated in long COVID.
• The prognostic model offers potential for early identification of high-risk patients and guides targeted therapy development.