Urine Metabolites Enable Fast Detection of COVID-19 Using Mass Spectrometry

Abstract

The COVID-19 pandemic boosted the development of diagnostic tests to meet patient needs and provide accurate, sensitive, and fast disease detection. Despite rapid advancements, limitations related to turnaround time, varying performance metrics due to different sampling sites, illness duration, co-infections, and the need for particular reagents still exist. As an alternative diagnostic test, we present urine analysis through flow-injection-tandem mass spectrometry (FIA-MS/MS) as a powerful approach for COVID-19 diagnosis, targeting the detection of amino acids and acylcarnitines. We adapted a method that is widely used for newborn screening tests on dried blood for urine samples in order to detect metabolites related to COVID-19 infection. We analyzed samples from 246 volunteers with diagnostic confirmation via PCR. Urine samples were self-collected, diluted, and analyzed with a run time of 4 min. A Lasso statistical classifier was built using 75/25% data for training/validation sets and achieved high diagnostic performances: 97/90% sensitivity, 95/100% specificity, and 95/97.2% accuracy. Additionally, we predicted on two withheld sets composed of suspected hospitalized/symptomatic COVID-19-PCR negative patients and patients out of the optimal time-frame collection for PCR diagnosis, with promising results. Altogether, we show that the benchmarked FIA-MS/MS method is promising for COVID-19 screening and diagnosis, and is also potentially useful after the peak viral load has passed.

Keywords: COVID-19; amino acids; diagnostic; metabolomics; urine.

Figure 1

The statistical classifier’s experimental design and performance when identifying healthy non-hospitalized COVID-19 PCR-negative (Neg-NH) volunteers or hospitalized COVID-19 PCR-positive (Pos-H) patients. The Withheld Set 1 was composed of suspected hospitalized/symptomatic COVID-19 PCR-negative (Neg-H) patients. In contrast, the Withheld Set 2 was composed of Pos-H and Neg-H patients who did not meet the time frame criteria.

Figure 2

Metabolites selected by Lasso analysis, the multiple reaction monitoring (MRM) transitions of detection (precursor > fragment), relative standard deviation (RSD) for quality control (QC) samples, and their weights for the Lasso model. * Acylcarnitines are expressed by the number of carbons on the chain.

Figure 3

Box plots for the analytes found as discriminatory by the Lasso model and their abundance between the three classes of volunteers: healthy non-hospitalized COVID-19 PCR-negative (Neg-NH) volunteers, hospitalized COVID-19 PCR-positive (Pos-H) patients and suspected hospitalized/symptomatic COVID-19 PCR-negative (Neg-H) patients. Univariate analysis was performed using the Kruskal–Wallis test. If a p-value is less than 0.05, it is flagged with one star (*). If a p-value is less than 0.01, it is flagged with 2 stars (**). If a p-value is less than 0.001, it is flagged with three stars (***). If a p-value is less than 0.0001, it is flagged with four stars (****).

Figure 4

Metabolite enrichment analysis via over representation analysis for the discriminatory analytes found by Lasso analysis. A set of human metabolites from the KEGG library was used, and the p-adjusted values for the pathways are presented by the color bar, with the significant ones (FDR < 0.05) displayed numerically.