Kynurenine and Hemoglobin as Sex-Specific Variables in COVID-19 Patients: A Machine Learning and Genetic Algorithms Approach

被引：8

作者：

Celaya-Padilla, Jose M. ^{[1
]}

Villagrana-Banuelos, Karen E. ^{[1
]}

Oropeza-Valdez, Juan Jose ^{[2
]}

Monarrez-Espino, Joel ^{[3
]}

Castaneda-Delgado, Julio E. ^{[2
,4
]}

Oostdam, Ana Sofia Herrera-Van ^{[5
]}

Fernandez-Ruiz, Julio Cesar ^{[2
]}

Ochoa-Gonzalez, Fatima ^{[2
,6
]}

Borrego, Juan Carlos ^{[7
]}

Enciso-Moreno, Jose Antonio ^{[2
]}

Lopez, Jesus Adrian ^{[8
]}

Lopez-Hernandez, Yamile ^{[4
,9
]}

Galvan-Tejada, Carlos E. ^{[1
]}

机构：

[1] Univ Autonoma Zacatecas, Unidad Acad Ingn Elect, Jardin Juarez 147, Zacatecas 98000, Zacatecas, Mexico

[2] Inst Mexicano Seguro Social, Unidad Invest Biomed Zacatecas, Zacatecas 98000, Zacatecas, Mexico

[3] Univ Monterrey, Christus Muguerza Parque Hosp Chihuahua, Dept Hlth Res, San Pedro Garza Garcia 66238, Mexico

[4] Consejo Nacl Ciencia & Technol, Catedras CONACyT, Mexico City 03940, DF, Mexico

[5] Univ Autonoma San Luis Potosi, Ctr Invest Ciencias Salud & Biomed, Doctorado Ciencias Biomed Basicas, San Luis Potosi 78210, San Luis Potosi, Mexico

[6] Univ Autonoma Zacatecas, Area Ciencias Salud, Carretera Zacatecas Guadalajara Kilometro 6, Zacatecas 98160, Zacatecas, Mexico

[7] Hosp Gen Zona 1 Emilio Varela Lujan, Inst Mexicano Seguro Social, Dept Epidemiol, Zacatecas 98000, Zacatecas, Mexico

[8] Univ Autonoma Zacatecas, Unidad Acad Ciencias Biol, Lab MicroRNAs & Canc, Zacatecas 98000, Zacatecas, Mexico

[9] Autonomous Univ Zacatecas, Metabol & Prote Lab, Zacatecas 98000, Zacatecas, Mexico

来源：

DIAGNOSTICS | 2021年 / 11卷 / 12期

关键词：

COVID-19; sex; machine learning; metabolomics; MORTALITY; PREDICTS; BIOLOGY; ANEMIA; SVM;

D O I：

10.3390/diagnostics11122197

中图分类号：

R5 [内科学];

学科分类号：

1002 ; 100201 ;

摘要：

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 biomarkers 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.

引用

页数：30

共 69 条

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