Changes in the Urine Metabolomic Profile in Patients Recovering from Severe COVID-19

Abstract

Metabolomics is a relatively new research area that focuses mostly on the profiling of selected molecules and metabolites within the organism. A SARS-CoV-2 infection itself can lead to major disturbances in the metabolite profile of the infected individuals. The aim of this study was to analyze metabolomic changes in the urine of patients during the acute phase of COVID-19 and approximately one month after infection in the recovery period. We discuss the observed changes in relation to the alterations resulting from changes in the blood plasma metabolome, as described in our previous study. The metabolome analysis was performed using NMR spectroscopy from the urine of patients and controls. The urine samples were collected at three timepoints, namely upon hospital admission, during hospitalization, and after discharge from the hospital. The acute COVID-19 phase induced massive alterations in the metabolic composition of urine was linked with various changes taking place in the organism. Discriminatory analyses showed the feasibility of successful discrimination of COVID-19 patients from healthy controls based on urinary metabolite levels, with the highest significance assigned to citrate, Hippurate, and pyruvate. Our results show that the metabolomic changes persist one month after the acute phase and that the organism is not fully recovered.

Keywords: COVID-19; NMR; discrimination; ketosis; metabolomics; random forest; urine.

Figure 1

Timepoints of urine sampling from the COVID-19 patients.

Figure 2

PCA analyzes patients with COVID–19 disease at three various sampling times (Day 1, red; Day 5−8, orange; Day 29−54, blue) and controls (green). (A) All three sampling times together, (B) Day 1 against controls; (C) Day 5–8 against controls, (D) day 29–54 against controls. The relative concentrations of metabolites in urine were used as input variables for the algorithm.

Figure 3

Relative levels of urine metabolites in COVID-19 patients at three consecutive timepoints and controls, data normalized to the level of creatinine, values related to the median of controls set to 1. (A-Day 1; B-Day 5–8; C-Day 29–54, ctrl-controls).

Figure 4

The ROC curve derived from the random forest discriminatory algorithm for binary systems patients on the first day of hospitalization (A), one week (B), and one month later (C), as input variables were used concentrations of urine metabolites related to the level of creatinine.