A patient-centric modeling framework captures recovery from SARS-CoV-2 infection
© 2023. The Author(s)..
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.
| Errataetall: |
CommentIn: Nat Immunol. 2023 Feb;24(2):207-208. - PMID 36717724 |
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| Medienart: |
E-Artikel |
| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:24 |
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| Enthalten in: |
Nature immunology - 24(2023), 2 vom: 22. Feb., Seite 349-358 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Ruffieux, Hélène [VerfasserIn] |
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| Links: |
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| Themen: |
343-65-7 |
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| Anmerkungen: |
Date Completed 03.02.2023 Date Revised 25.04.2024 published: Print-Electronic CommentIn: Nat Immunol. 2023 Feb;24(2):207-208. - PMID 36717724 Citation Status MEDLINE |
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| doi: |
10.1038/s41590-022-01380-2 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| 520 | |a © 2023. The Author(s). | ||
| 520 | |a 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 | ||
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| 700 | 1 | |a Nolan, Tui H |e verfasserin |4 aut | |
| 700 | 1 | |a Bergamaschi, Laura |e verfasserin |4 aut | |
| 700 | 1 | |a Mescia, Federica |e verfasserin |4 aut | |
| 700 | 1 | |a Turner, Lorinda |e verfasserin |4 aut | |
| 700 | 1 | |a de Sa, Aloka |e verfasserin |4 aut | |
| 700 | 1 | |a Pelly, Victoria S |e verfasserin |4 aut | |
| 700 | 0 | |a Cambridge Institute of Therapeutic Immunology and Infectious Disease-National Institute of Health Research (CITIID-NIHR) BioResource COVID-19 Collaboration |e verfasserin |4 aut | |
| 700 | 1 | |a Kotagiri, Prasanti |e verfasserin |4 aut | |
| 700 | 1 | |a Kingston, Nathalie |e verfasserin |4 aut | |
| 700 | 1 | |a Bradley, John R |e verfasserin |4 aut | |
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