Towards risk stratification and prediction of disease severity and mortality in COVID-19: Next generation metabolomics for the measurement of host response to COVID-19 infection

Abstract

This study investigated the association between COVID-19 infection and host metabolic signatures as prognostic markers for disease severity and mortality. We enrolled 82 patients with RT-PCR confirmed COVID-19 infection who were classified as mild, moderate, or severe/critical based upon their WHO clinical severity score and compared their results with 31 healthy volunteers. Data on demographics, comorbidities and clinical/laboratory characteristics were obtained from medical records. Peripheral blood samples were collected at the time of clinical evaluation or admission and tested by quantitative mass spectrometry to characterize metabolic profiles using selected metabolites. The findings in COVID-19 (+) patients reveal changes in the concentrations of glutamate, valeryl-carnitine, and the ratios of Kynurenine/Tryptophan (Kyn/Trp) to Citrulline/Ornithine (Cit/Orn). The observed changes may serve as predictors of disease severity with a (Kyn/Trp)/(Cit/Orn) Receiver Operator Curve (ROC) AUC = 0.95. Additional metabolite measures further characterized those likely to develop severe complications of their disease, suggesting that underlying immune signatures (Kyn/Trp), glutaminolysis (Glutamate), urea cycle abnormalities (Cit/Orn) and alterations in organic acid metabolism (C5) can be applied to identify individuals at the highest risk of morbidity and mortality from COVID-19 infection. We conclude that host metabolic factors, measured by plasma based biochemical signatures, could prove to be important determinants of Covid-19 severity with implications for prognosis, risk stratification and clinical management.

Fig 1. Flowchart illustrating workflow and data processing.

Individual metabolite absolute concentrations measured by targeted mass spectrometry (MS/MS) transmitted in.csv data-files were log transformed for normalization and then uploaded into MetaboAnalyst 5.0 bio-informatic data analytic platform. Univariate (t-test, ANOVA), multivariate (PCA, PLS-DA, Heatmaps, Multivariate ROC Curve Analysis) and correlation coefficients (Pearson r) then applied to identify metabolites and ratios associated with COVID-19.

Fig 2

(A, B) reflect unsupervised clustering analysis using the most discriminating ratios that segregate controls (n = 31) from COVID-19 (+) patients (n = 77). The average accuracy based on 100 cross validations is 0.95 with an ROC AUC = 0.975 (95% CI 0.889–0.999) and permutation test statistic: p<0.0001.

Fig 3

(A, B) provide base line predictions separating mild from moderate/severe using multivariate ROC Curve analysis applying the ratios obtained from the training set (Fig 2A and 2B): [(Glu/PC ae C42:1)/Taurine] and [IDO/(Cit/Orn)]/(PC ae C36:4). The average accuracy based on 100 cross validations is 0.90, Permutation Test (x500) statistics = p< 7.10e-05.

Fig 4. Heatmap of unsupervised clustering analysis using 30 most discriminating metabolites and ratios comparing mild (red) vs moderate/severe (green) Covid 19 outcomes.

Fig 5. Ratio of immune dysfunction reflected by indole oxygenase activity (Kyn/Trp) over liver dysfunction reflected by ornithine transcarbamylase (Cit/Orn) discriminates patients with mild vs moderate/severe outcomes.

100-fold Cross Validation = 0.82, Predictive Accuracy (100 permutations) p = 1.00E-03.

Fig 6

(A-D). Comparison of immune signatures for Covid 19 vs. HIV using immune IDO (Kyn/Trp) ratio divided by the inflammatory markers (lyso PC a 18:2 and 18:0) correlates Covid 19 severity with HIV progression.

Fig 7

(A, B). Pearson Moment correlations of IDO activity (Kyn/Trp) and Ornithine transcarbamylase activity (Cit/Orn) for disease severity comparing controls, mild, moderate, and severe Covid patients.

Fig 8

(A, B). Glutamate and Valeryl-carnitine (C5) concentrations comparing controls (red) n = 36 to Covid 19 patients (green) n = 77 provide ROC AUC = 0.85 (95% CI 0.764–0.92) and AUC = 0.799 (95% CI 0.715–0.875) respectively.