Biomarker-Based Risk Assessment Strategy for Long COVID: Leveraging Spike Protein and Proinflammatory Mediators to...

Area of Science:

• Immunology
• Virology
• Mathematical Biology

Background:

• Long COVID presents persistent symptoms post-SARS-CoV-2 infection, posing significant public health and socioeconomic challenges.
• Effective risk assessment is crucial for identifying and managing individuals at risk of prolonged symptoms.
• Understanding the relationship between viral load and symptom development is key to mitigating Long COVID's impact.

Purpose of the Study:

• To quantitatively characterize dose-response relationships between spike protein concentrations and Long COVID symptoms.
• To develop a mathematical model for estimating spike protein concentrations over time in Long COVID patients.
• To identify key inflammatory biomarkers and their effective doses related to Long COVID symptom severity.

Main Methods:

• Quantitative analysis of spike protein concentrations and Long COVID symptom data.
• Development of a mathematical model to track time-dependent spike protein levels.
• Cluster analysis and dose-response assessments for inflammatory mediators (CXCL8, IL-6, IL-1β, TNF-α).

Main Results:

• A maximum Long COVID symptom number of approximately 20 was estimated, correlating with spike protein concentration.
• CXCL8 demonstrated the lowest 50% effective dose (0.01 μg mL⁻¹) among tested proinflammatory mediators.
• Established dose-response curves and a predictive mathematical model for spike protein levels.

Conclusions:

• The study provides a comprehensive risk assessment strategy for Long COVID using dose-response tools and mathematical modeling.
• Findings support personalized care strategies and inform public health policies for early intervention.
• This research aids in reducing long-term disability and healthcare burdens associated with Long COVID and other post-infection syndromes.