Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Combining Semi-targeted Metabolomics and Machine Learning to Identify Metabolic Alterations in Serum and Urine of Hospitalized Patients with COVID-19

Gerard Baiges-Gaya
,
Simona Iftimie
* ORCID logo ,
Helena Castañé
,
Elisabet Rodríguez-Tomàs
,
Andrea Jiménez-Franco
ORCID logo ,
Ana F. López-Azcona
,
Antoni Castro
,
Jordi Camps
* ,
Jorge Joven
ORCID logo
Version 1 : Received: 24 November 2022 / Approved: 25 November 2022 / Online: 25 November 2022 (08:05:52 CET)

A peer-reviewed article of this Preprint also exists.

Baiges-Gaya, G.; Iftimie, S.; Castañé, H.; Rodríguez-Tomàs, E.; Jiménez-Franco, A.; López-Azcona, A.F.; Castro, A.; Camps, J.; Joven, J. Combining Semi-Targeted Metabolomics and Machine Learning to Identify Metabolic Alterations in the Serum and Urine of Hospitalized Patients with COVID-19. Biomolecules 2023, 13, 163. Baiges-Gaya, G.; Iftimie, S.; Castañé, H.; Rodríguez-Tomàs, E.; Jiménez-Franco, A.; López-Azcona, A.F.; Castro, A.; Camps, J.; Joven, J. Combining Semi-Targeted Metabolomics and Machine Learning to Identify Metabolic Alterations in the Serum and Urine of Hospitalized Patients with COVID-19. Biomolecules 2023, 13, 163.

Abstract

Viral infections cause metabolic dysregulation in the infected organism. The present study used metabolomics techniques and machine learning algorithms to retrospectively analyze the alter-ations of a broad panel of metabolites in the serum and urine of a cohort of 126 patients hospi-talized with COVID-19. Results were compared with those of 50 healthy subjects and 45 COVID-19 negative patients but with bacterial infectious diseases. Metabolites were analyzed by gas chro-matography coupled to quadrupole time-of-flight mass spectrometry. The main metabolites al-tered in the sera of COVID-19 patients were those of pentose glucuronate interconversion, ascorbate and fructose metabolism, nucleotide sugars, and nucleotide and amino acid metabolism. Alterations in serum maltose, mannonic acid, xylitol, or glyceric acid metabolites segregated positive patients from the control group with high diagnostic accuracy, while succinic acid seg-regated positive patients from those with other disparate infectious diseases. Increased lauric acid concentrations were associated with severity of infection and death. Urine analyses could not discriminate between groups. Targeted metabolomics and machine learning algorithms facilitated the exploration of the metabolic alterations underlying COVID-19 infection, and to identify po-tential biomarkers for the diagnosis and prognosis of the disease.

Keywords

Keywords: biomarkers; COVID-19; machine learning; metabolomics; SARS-CoV-2.

Subject

Medicine and Pharmacology, Endocrinology and Metabolism

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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