Metabolomic profiling of COVID-19 using serum and urine samples in intensive care and medical ward cohorts

Abstract

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, analysing 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. These discriminant metabolites play a role in specific pathways, mainly "Phenylalanine, tyrosine and tryptophan biosynthesis", "Phenylalanine metabolism", "Glycerolipid metabolism" and "Arginine and proline metabolism". We propose a three-metabolite diagnostic panel-comprising isoleucine, TMAO, and glucose-that effectively discriminates COVID-19 patients from healthy individuals, achieving high efficiency. Furthermore, we found an optimal biomarker panel capable of efficiently classify disease severity considering both clinical characteristics (obesity/overweight, dyslipidemia, and lymphocyte count) together with metabolites content (ethanol, TMAO, tyrosine and betaine).

Keywords: Biomarkers; COVID-19; Metabolomics; NMR; Serum; Urine.

Fig. 1

(A) PCA scores plot, (B) OPLS-DA scores plot and (C) important features (VIP-plot from OPLS-DA) distinguishing healthy controls and COVID-19 serum samples. Scaling was done to Pareto. R²X = 0.79, R²Y = 0.90, Q² (cum) = 0.83, CV-ANOVA = 2.12 x 10^-32 for OPLS-DA, which was validated through Permutation Test (Figure S2). Important features (VIP > 1) plot shows those buckets that contain metabolites that increase (in red) or decrease (in blue) its content for each group. (D) Significantly enriched metabolic routes by COVID-19 built with serum metabolites with VIP > 1 from OPLS-DA model: 1) Phenylalanine, tyrosine and tryptophan biosynthesis, 2) Phenylalanine metabolism, 3) Glycerolipid metabolism and 4) Arginine and proline metabolism. The more affected metabolic pathways appear with a color gradient, from yellow (less significant) to red (most significant) and satisfy the following criteria: number of matching metabolites in the pathway > 1, false discovery rate (FDR) adjusted p < 0.05 and impact index > 0 (see Table S5).

Fig. 2

(A) PCA scores plot, (B) OPLS-DA scores plot, and (C) important features (VIP-plot from OPLS-DA) distinguishing healthy controls and COVID-19 urine samples. Scaling was done to Pareto. R²X = 0.72, R²Y = 0.85, Q² (cum) = 0.73, CV-ANOVA = 1.01 × 10^− 21 for OPLS-DA model which was validated through Permutation Test (Figure S3). Important features (VIP > 1) plot shows those buckets that contain metabolites that increase (in red) or decrease (in blue) its content for each group.

Fig. 3

Relations between clinical features and serum metabolite levels. (A) Scatterplot on correlation between serum glucose levels in COVID-19 patients and hospital time (R² = 0.055, correlation coefficient (r) = 0.235, p-value < 0.05). Correlation analysis between clinical features and serum metabolite levels in COVID-19 patients (B) Considering MW and ICU groups together. (C) COVID-19 patients admitted to the MW. (D) COVID-19 patients admitted to the ICU. The color scale shows whether the relationship between two variables is positive (blue) or negative (violet). Spearman’s correlation coefficient was used for the analysis. Abbreviations: TAG: triacylglycerol, PC: phosphocholine, GPC: glycerophosphocholine, FA: fatty acids, PUFA: polyunsaturated FA, TMAO: trimethylamine, NAG: N-acetylglutamate, UFA: unsaturated FA, LDH: lactate dehydrogenase, IL-6: interleukin 6.