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A patient-centric modeling framework captures recovery from SARS-CoV-2 infection
Hélène Ruffieux1, Aimee L Hanson2,3, Samantha Lodge4,5
1MRC Biostatistics Unit, University of Cambridge, Cambridge Biomedical Campus, Cambridge, UK. helene.ruffieux@mrc-bsu.cam.ac.uk.
Nature Immunology
|January 30, 2023
Summary
Understanding severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) recovery is crucial. This study identified distinct patient recovery profiles and predictive signatures for systemic recovery, guiding future long COVID research.
Area of Science:
- Immunology
- Metabolomics
- Systems Biology
Background:
- Individual responses to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection are not fully understood.
- Longitudinal data are needed to characterize the recovery trajectories of infected patients.
Purpose of the Study:
- To develop a patient-centric framework for analyzing systemic recovery after SARS-CoV-2 infection.
- To identify biological signatures that predict recovery outcomes and long COVID risk.
Main Methods:
- Longitudinal phenotyping of 215 individuals with varying disease severity for one year post-infection.
- Analysis of inflammatory, immune cell, metabolic, and clinical parameters.
- Development of a composite signature using a joint model of early cellular and molecular data.
Main Results:
- Distinct systemic recovery profiles were observed, characterized by specific inflammatory, immune, metabolic, and clinical trajectories.
- Strong temporal covariation was found between innate immune cells, kynurenine metabolites, and lipid metabolites.
- A predictive signature for systemic recovery was identified using early post-disease onset parameters.
Conclusions:
- The study provides insights into the complex biology of SARS-CoV-2 recovery and identifies key factors influencing homeostasis restoration.
- The identified signature can predict systemic recovery and may help stratify risk for long COVID.
- An online tool is available for prospective testing of these findings.
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