Kynurenine and hemoglobin as sex-specific variables in COVID-19 patients: A machine learning and genetic algorithms approach

Jose M. Celaya-Padilla, Karen E. Villagrana-Bañuelos, Juan José Oropeza-Valdez, Joel Monárrez-Espino, Julio E. Castañeda-Delgado, Ana Sofía Herrera Van Oostdam, Julio César Fernández-Ruiz, Fátima Ochoa-González, Juan Carlos Borrego, Jose Antonio Enciso-Moreno, Jesús Adrián López, Yamilé López-Hernández, Carlos E. Galván-Tejada

Research output: Contribution to journalArticlepeer-review

Abstract

Differences in clinical manifestations, immune response, metabolic alterations, and outcomes (including disease severity and mortality) between men and women with COVID-19 have been reported since the pandemic outbreak, making it necessary to implement sex-specific biomark-ers for disease diagnosis and treatment. This study aimed to identify sex-associated differences in COVID-19 patients by means of a genetic algorithm (GALGO) and machine learning, employing support vector machine (SVM) and logistic regression (LR) for the data analysis. Both algorithms identified kynurenine and hemoglobin as the most important variables to distinguish between men and women with COVID-19. LR and SVM identified C10:1, cough, and lysoPC a 14:0 to discriminate between men with COVID-19 from men without, with LR being the best model. In the case of women with COVID-19 vs. women without, SVM had a higher performance, and both models identified a higher number of variables, including 10:2, lysoPC a C26:0, lysoPC a C28:0, alpha-ketoglutaric acid, lactic acid, cough, fever, anosmia, and dysgeusia. Our results demonstrate that differences in sexes have implications in the diagnosis and outcome of the disease. Further, genetic and machine learning algorithms are useful tools to predict sex-associated differences in COVID-19.

Original languageEnglish
Article number2197
JournalDiagnostics
Volume11
Issue number12
DOIs
Publication statusPublished - Dec 2021

Bibliographical note

Funding Information:
Funding: This research was funded by CONACyT grant number 311880 and 316258.

Publisher Copyright:
© 2021 by the authors. Licensee MDPI, Basel, Switzerland.

All Science Journal Classification (ASJC) codes

  • Clinical Biochemistry

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