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 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.
Highlights
A total of 117 patients with confirmed COVID-19 and 40 negative individuals used as controls were enrolled in this study
This work reinforces the need to take into account the sex differences to accurately diagnose COVID-19 infection
We found that different metabolites are needed to discern COVID-19 in women and in men
Summary
A recent meta-analysis of 3.1 million global cases showed that men have a nearly three times higher chance of being admitted to an intensive care unit (ICU) and a higher risk of dying, even though the incidence of COVID-19 infection is similar [6]. Support vector machine (SVM), firstly proposed by Vapnik [16] has proved to be a powerful technique for pattern recognition, classification, and regression in many fields [17,18,19,20]. To obtain accurate class predictions, SVMs provide a number of free parameters that have to be tuned to reflect the requirements of a given task
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