Biological and Clinical Factors Contributing to the Metabolic Heterogeneity of Hospitalized Patients with and without COVID-19.

Autor: D'Alessandro A; Department of Biochemistry and Molecular Genetics, Anschutz Medical Campus, University of Colorado Denver, Aurora, CO 80045, USA., Thomas T; Department of Pathology & Cell Biology, Columbia University Vagelos College of Physicians and Surgeons, New York, NY 10032, USA., Akpan IJ; Division of Hematology/Oncology, Department of Medicine, Irving Medical Center, Columbia University, New York, NY 10032, USA., Reisz JA; Department of Biochemistry and Molecular Genetics, Anschutz Medical Campus, University of Colorado Denver, Aurora, CO 80045, USA., Cendali FI; Department of Biochemistry and Molecular Genetics, Anschutz Medical Campus, University of Colorado Denver, Aurora, CO 80045, USA., Gamboni F; Department of Biochemistry and Molecular Genetics, Anschutz Medical Campus, University of Colorado Denver, Aurora, CO 80045, USA., Nemkov T; Department of Biochemistry and Molecular Genetics, Anschutz Medical Campus, University of Colorado Denver, Aurora, CO 80045, USA., Thangaraju K; Center for Blood Oxygen Transport, Department of Pathology, Department of Pediatrics, University of Maryland, Baltimore, MD 21201, USA., Katneni U; Center for Blood Oxygen Transport, Department of Pathology, Department of Pediatrics, University of Maryland, Baltimore, MD 21201, USA., Tanaka K; Department of Anesthesiology, University of Maryland, Baltimore, MD 21201, USA.; Department of Anesthesiology, University of Oklahoma College of Medicine, Oklahoma City, OK 73126-0901, USA., Kahn S; Division of Hematology/Oncology, Department of Medicine, Irving Medical Center, Columbia University, New York, NY 10032, USA., Wei AZ; Division of Hematology/Oncology, Department of Medicine, Irving Medical Center, Columbia University, New York, NY 10032, USA., Valk JE; Department of Pathology & Cell Biology, Columbia University Vagelos College of Physicians and Surgeons, New York, NY 10032, USA., Hudson KE; Department of Pathology & Cell Biology, Columbia University Vagelos College of Physicians and Surgeons, New York, NY 10032, USA., Roh D; Department of Neurology, Columbia University Vagelos College of Physicians and Surgeons, New York, NY 10032, USA., Moriconi C; Department of Pathology & Cell Biology, Columbia University Vagelos College of Physicians and Surgeons, New York, NY 10032, USA., Zimring JC; Department of Pathology, University of Virginia, Charlottesville, VA 22903, USA., Hod EA; Department of Pathology & Cell Biology, Columbia University Vagelos College of Physicians and Surgeons, New York, NY 10032, USA., Spitalnik SL; Department of Pathology & Cell Biology, Columbia University Vagelos College of Physicians and Surgeons, New York, NY 10032, USA., Buehler PW; Center for Blood Oxygen Transport, Department of Pathology, Department of Pediatrics, University of Maryland, Baltimore, MD 21201, USA., Francis RO; Department of Pathology & Cell Biology, Columbia University Vagelos College of Physicians and Surgeons, New York, NY 10032, USA.
Jazyk: angličtina
Zdroj: Cells [Cells] 2021 Sep 02; Vol. 10 (9). Date of Electronic Publication: 2021 Sep 02.
DOI: 10.3390/cells10092293
Abstrakt: The Corona Virus Disease 2019 (COVID-19) pandemic represents an ongoing worldwide challenge. The present large study sought to understand independent and overlapping metabolic features of samples from acutely ill patients (n = 831) that tested positive (n = 543) or negative (n = 288) for COVID-19. High-throughput metabolomics analyses were complemented with antigen and enzymatic activity assays on plasma from acutely ill patients collected while in the emergency department, at admission, or during hospitalization. Lipidomics analyses were also performed on COVID-19-positive or -negative subjects with the lowest and highest body mass index (n = 60/group). Significant changes in amino acid and fatty acid/acylcarnitine metabolism emerged as highly relevant markers of disease severity, progression, and prognosis as a function of biological and clinical variables in these patients. Further, machine learning models were trained by entering all metabolomics and clinical data from half of the COVID-19 patient cohort and then tested on the other half, yielding ~78% prediction accuracy. Finally, the extensive amount of information accumulated in this large, prospective, observational study provides a foundation for mechanistic follow-up studies and data sharing opportunities, which will advance our understanding of the characteristics of the plasma metabolism in COVID-19 and other acute critical illnesses.
Databáze: MEDLINE