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

Kaiming Wang; Mobin Khoramjoo; Karthik Srinivasan; Paul M K Gordon; Rupasri Mandal; Dana Jackson; Wendy Sligl; Maria B Grant; Josef M Penninger; Christoph H Borchers; David S Wishart; Vinay Prasad; Gavin Y Oudit

doi:10.1016/j.xcrm.2023.101254

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

Cell Rep Med. 2023 Nov 21;4(11):101254. doi: 10.1016/j.xcrm.2023.101254. Epub 2023 Oct 26.

Authors

Affiliations

¹ Division of Cardiology, Department of Medicine, University of Alberta, Edmonton, AB, Canada; Mazankowski Alberta Heart Institute, University of Alberta, Edmonton, AB, Canada; Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.
² Mazankowski Alberta Heart Institute, University of Alberta, Edmonton, AB, Canada; Department of Physiology, University of Alberta, Edmonton, AB, Canada.
³ Department of Chemical and Materials Engineering, University of Alberta, Edmonton, AB, Canada.
⁴ Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.
⁵ The Metabolomics Innovation Center, University of Alberta, Edmonton, AB, Canada.
⁶ Division of Cardiology, Department of Medicine, University of Alberta, Edmonton, AB, Canada; Mazankowski Alberta Heart Institute, University of Alberta, Edmonton, AB, Canada.
⁷ Department of Critical Care Medicine, University of Alberta, Edmonton, AB, Canada; Division of Infectious Diseases, Department of Medicine, University of Alberta, Edmonton, AB, Canada.
⁸ Department of Ophthalmology and Visual Sciences, University of Alabama at Birmingham, Birmingham, AL, USA.
⁹ Department of Medical Genetics, Life Sciences Institute, University of British Columbia, Vancouver, BC, Canada; Institute of Molecular Biotechnology of the Austrian Academy of Sciences (IMBA), Vienna, Austria.
¹⁰ Segal Cancer Proteomics Centre, Lady Davis Institute for Medical Research, Jewish General Hospital, McGill University, Montreal, QC, Canada; Gerald Bronfman Department of Oncology, McGill University, Montreal, QC, Canada.
¹¹ Division of Cardiology, Department of Medicine, University of Alberta, Edmonton, AB, Canada; Mazankowski Alberta Heart Institute, University of Alberta, Edmonton, AB, Canada; Department of Physiology, University of Alberta, Edmonton, AB, Canada. Electronic address: gavin.oudit@ualberta.ca.

Abstract

The post-acute sequelae of COVID-19 (PASC), also known as long COVID, is often associated with debilitating symptoms and adverse multisystem consequences. We obtain plasma samples from 117 individuals during and 6 months following their acute phase of infection to comprehensively profile and assess changes in cytokines, proteome, and metabolome. Network analysis reveals sustained inflammatory response, platelet degranulation, and cellular activation during convalescence accompanied by dysregulation in arginine biosynthesis, methionine metabolism, taurine metabolism, and tricarboxylic acid (TCA) cycle processes. Furthermore, we develop a prognostic model composed of 20 molecules involved in regulating T cell exhaustion and energy metabolism that can reliably predict adverse clinical outcomes following discharge from acute infection with 83% accuracy and an area under the curve (AUC) of 0.96. Our study reveals pertinent biological processes during convalescence that differ from acute infection, and it supports the development of specific therapies and biomarkers for patients suffering from long COVID.

Keywords: COVID-19; SARS-CoV-2; biomarkers; long COVID; machine learning; metabolomics; outcomes; phenotyping; post-acute sequelae of COVID-19; proteomics.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Biomarkers
COVID-19*
Convalescence
Humans
Multiomics
Phenotype
Post-Acute COVID-19 Syndrome*

Substances

Biomarkers