A patient-centric modeling framework captures recovery from SARS-CoV-2 infection

The biology driving individual patient responses to severe acute respiratory syndrome coronavirus 2 infection remains ill understood. Here, we developed a patient-centric framework leveraging detailed longitudinal phenotyping data and covering a year after disease onset, from 215 infected individuals with differing disease severities. Our analyses revealed distinct 'systemic recovery' profiles, with specific progression and resolution of the inflammatory, immune cell, metabolic and clinical responses. In particular, we found a strong inter-patient and intra-patient temporal covariation of innate immune cell numbers, kynurenine metabolites and lipid metabolites, which highlighted candidate immunologic and metabolic pathways influencing the restoration of homeostasis, the risk of death and that of long COVID. Based on these data, we identified a composite signature predictive of systemic recovery, using a joint model on cellular and molecular parameters measured soon after disease onset. New predictions can be generated using the online tool http://shiny.mrc-bsu.cam.ac.uk/apps/covid-19-systemic-recovery-prediction-app, designed to test our findings prospectively.

Automated summary

We analyzed detailed longitudinal phenotyping data from 215 individuals with varying disease severities to understand the biology behind individual patient responses to SARS-CoV-2 infection. Our findings revealed distinct profiles of 'systemic recovery', including the progression and resolution of inflammatory, immune cell, metabolic, and clinical responses. We identified strong correlations between innate immune cell numbers, kynurenine metabolites, and lipid metabolites, which have implications for homeostasis restoration, risk of death, and long COVID.