A metabolic readout of the urine metabolome of COVID-19 patients

Abstract

Analysis of urine samples from COVID-19 patients by ¹H NMR reveals important metabolic alterations due to SAR-CoV-2 infection. Previous studies have identified biomarkers in urine that reflect metabolic alterations in COVID-19 patients. We have used ¹H NMR to better define these metabolic alterations since this technique allows us to obtain a broad profile of the metabolites present in urine. This technique offers the advantage that sample preparation is very simple and gives us very complete information on the metabolites present. To detect these alterations, we have compared urine samples from COVID-19 patients (n = 35) with healthy people (n = 18). We used unsupervised (Robust PCA) and supervised (PLS-LDA) multivariate analysis methods to evaluate the differences between the two groups: COVID-19 and healthy controls. The differences focus on a group of metabolites related to energy metabolism (glucose, ketone bodies, glycine, creatinine, and citrate) and other processes related to bacterial flora (TMAO and formic acid) and detoxification (hippuric acid). The alterations in the urinary metabolome shown in this work indicate that SARS-CoV-2 causes a metabolic change from a normal situation of glucose consumption towards a gluconeogenic situation and possible insulin resistance.

Keywords: COVID-19; Gut microbiota; Human metabolism; Metabolomics.

Fig. 1

ROBPCA score plots for urine spectrum data for COVID-19 patients (blue circles) and healthy controls (red diamonds)

Fig. 2

A First two components of the PLS-LDA model score plots for urine ¹H NMR spectra for COVID-19 patients (blue circles) and healthy controls (red diamonds). B Predicted COVID-19 or healthy by PLS-LDA model using urine ¹H NMR spectra, 3 LVs and pareto scaling as data pretreatment. C Pseudospectrum format PLS-LDA tpLoading for the urine ¹H NMR spectra for COVID-19 patients and healthy controls. The intensity of the peaks (positive or negative) in the pseudospectrum significant the most significant spectral shift regions in the PLS-LDA model. The more significant peaks are identified in the pseudospectrum

Fig. 3

Plot samples in PLS-LDA component space with separating line shown. Train data set: COVID-19 patients (red diamonds) and healthy controls (blue circles). Test data set: COVID-19 patients (black diamonds) and patients whit a chronic viral disease (green circles)