Exploring metabolic anomalies in COVID-19 and post-COVID-19: a machine learning approach with explainable artificial intelligence - Oropeza-Valdez, Juan José; Padron-Manrique, Cristian; Vázquez-Jiménez, Aarón; Soberon, Xavier; Resendis-Antonio, Osbaldo (Frontiers in Molecular Biosciences, 2024)
The COVID-19 pandemic, caused by SARS-CoV-2, has led to significant challenges worldwide, including diverse clinical outcomes and prolonged post-recovery symptoms known as Long COVID or Post-COVID-19 syndrome. Emerging evidence suggests a crucial role of metabolic reprogramming in the infection's long-term consequences. This study employs a novel approach utilizing machine learning (ML) and explainable artificial intelligence (XAI) to analyze metabolic alterations in COVID-19 and Post-COVID-19 patients. By integrating ML with SHAP (SHapley Additive exPlanations) values, we uncover metabolomic signatures and identify potential biomarkers for these conditions. Our analysis included samples from a previous cohort integrated by 142 COVID-19, 48 Post-COVID-19, and 38 CONTROL patients, all comprising 111 identified metabolites. Traditional analysis methods, like PCA and PLS-DA, were compared with advanced ML techniques to discern metabolic changes. Notably, eXtreme Gradient Boosting (XGBoost), enhanced by SHAP for explainability, outperformed traditional methods. This ML method demonstrates superior predictive performance and provides new insights into the metabolic basis of the disease's progression and its aftermath. In particular, our analysis revealed several metabolomic subgroups within the COVID-19 and Post-COVID-19 conditions suggesting heterogeneous metabolic responses to the infection and its long-term impacts. Key metabolic signatures found in Post-COVID-19 include taurine, glutamine, alpha-Ketoglutaric acid and LysoPC a C16:0. This study highlights the potential of integrating ML and XAI for a fine-grain description in metabolomics research.