Metabolomic Profiling of Covid-19 Using Serum and Urine Samples in Intensive Care and Medical Ward Cohorts

The COVID-19 pandemic remains a significant global health threat, with uncertainties persisting regarding the factors determining whether individuals experience mild symptoms, severe conditions, or succumb to the disease. This study presents an NMR metabolomics-based approach, analyzing 80 serum and urine samples from COVID-19 patients (34 intensive care patients and 46 hospitalized patients) and 32 from healthy controls. Our research identifies discriminant metabolites and clinical variables relevant to COVID-19 diagnosis and severity. We propose a three-metabolite diagnostic panel—comprising isoleucine, TMAO, and glucose—that effectively discriminates COVID-19 patients from healthy individuals, achieving high efficiency. Recognizing that serum profiles are more reliable but invasive compared to urine samples, we propose reconstructing serum profiles using urine 1H NMR data. Our robust multi-output regression model demonstrates high accuracy in this reconstruction, and in classifying the converted serum spectroscopic profile. This suggests the feasibility of determining COVID-19 infection and predicting its severity using a non-invasive sample such as urine.