Integrative metabolomic and proteomic signatures define clinical outcomes in severe COVID-19.

TitleIntegrative metabolomic and proteomic signatures define clinical outcomes in severe COVID-19.
Publication TypeJournal Article
Year of Publication2022
AuthorsBuyukozkan M, Alvarez-Mulett S, Racanelli AC, Schmidt F, Batra R, Hoffman KL, Sarwath H, Engelke R, Gomez-Escobar L, Simmons W, Benedetti E, Chetnik K, Zhang G, Schenck E, Suhre K, Choi JJ, Zhao Z, Racine-Brzostek S, Yang HS, Choi ME, Choi AMK, Cho SJung, Krumsiek J
Date Published2022 Jul 15

The coronavirus disease-19 (COVID-19) pandemic has ravaged global healthcare with previously unseen levels of morbidity and mortality. In this study, we performed large-scale integrative multi-omics analyses of serum obtained from COVID-19 patients with the goal of uncovering novel pathogenic complexities of this disease and identifying molecular signatures that predict clinical outcomes. We assembled a network of protein-metabolite interactions through targeted metabolomic and proteomic profiling in 330 COVID-19 patients compared to 97 non-COVID, hospitalized controls. Our network identified distinct protein-metabolite cross talk related to immune modulation, energy and nucleotide metabolism, vascular homeostasis, and collagen catabolism. Additionally, our data linked multiple proteins and metabolites to clinical indices associated with long-term mortality and morbidity. Finally, we developed a novel composite outcome measure for COVID-19 disease severity based on metabolomics data. The model predicts severe disease with a concordance index of around 0.69, and shows high predictive power of 0.83-0.93 in two independent datasets.

Alternate JournaliScience
PubMed ID35756895
PubMed Central IDPMC9212983
Grant ListK08 HL138285 / HL / NHLBI NIH HHS / United States
U19 AG063744 / AG / NIA NIH HHS / United States
Related Faculty: 
He Sarina Yang, M.D., Ph.D.


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