Main blood serum metabolites responsible for the discrimination of hospitalized patients with moderate symptoms with or without COVID-19

2021 
Background : The variation in human blood serum metabolites resulting from an infection can improve diagnosis. Aims : To map serum signatures of hospitalized symptomatic patients, positive or negative to SARS-CoV-2. Methods : Patients ( n = 64) admitted to Anhembi Field Municipal Hospital, a hospital set up for initial care to patients with moderate symptoms, were analyzed being discriminated in positive ( n = 32) or negative. Age and gender were matched to ensure homogeneity in the basal metabolic rates. Three Nuclear Magnetic Resonance (NMR) data set were recorded on Bruker AVANCE III spectrometer for serum samples analyzed in MetaboAnalyst 5.0 software platform. Results : The results for positive and negative patients were: mean age 54.92 ± 12.41 and 54.30 ± 12.15, and 50% female in each group. The ethnicity was 56.2% vs . 46.8% caucasian, 34.3% mixed race in both groups, and 9.3% vs . 12.5% black in positive and negative groups, respectively. BMI was 24 ± 6.93 vs . 33.5 ± 7.85 in comparison to positive and negative patients, respectively. In both groups 50% of patients presented alveolar infiltrate. Although the groups were not paired by comorbidities, they were homogeneous ensuring that the metabolic variation is due to COVID-19. Clinical symptoms were also remarkably similar between the groups (Table 1). The Partial Least Squares -Discriminant Analysis (PLS-DA) performed onto noesy1d data discriminated positively from negative patients (Figure 1.A). Also, it covered lower variance (Figure 1.B). Combining NMR techniques, it was possible to depict the main metabolites that distinguished the COVID-19 signatures. Alanine, glucose, cholesterol, and glutamine were increased, and lactate decreased (Figure 1.C.) in COVID-19. Conclusions : These results suggest NMR as an excellent tool to differentiate hospitalized patients with moderate symptoms as COVID-19 positive or negative.
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