TY - JOUR
T1 - Kynurenine and hemoglobin as sex-specific variables in COVID-19 patients
T2 - A machine learning and genetic algorithms approach
AU - Celaya-Padilla, Jose M.
AU - Villagrana-Bañuelos, Karen E.
AU - Oropeza-Valdez, Juan José
AU - Monárrez-Espino, Joel
AU - Castañeda-Delgado, Julio E.
AU - Oostdam, Ana Sofía Herrera Van
AU - Fernández-Ruiz, Julio César
AU - Ochoa-González, Fátima
AU - Borrego, Juan Carlos
AU - Enciso-Moreno, Jose Antonio
AU - López, Jesús Adrián
AU - López-Hernández, Yamilé
AU - Galván-Tejada, Carlos E.
N1 - Funding Information:
Funding: This research was funded by CONACyT grant number 311880 and 316258.
Publisher Copyright:
© 2021 by the authors. Licensee MDPI, Basel, Switzerland.
PY - 2021/12
Y1 - 2021/12
N2 - 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.
AB - 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.
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U2 - 10.3390/diagnostics11122197
DO - 10.3390/diagnostics11122197
M3 - Article
AN - SCOPUS:85120166770
SN - 2075-4418
VL - 11
JO - Diagnostics
JF - Diagnostics
IS - 12
M1 - 2197
ER -