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. 2022 Jan;12(1):e704.
doi: 10.1002/ctm2.704.

Fetuin-A, inter-α-trypsin inhibitor, glutamic acid and ChoE (18:0) are key biomarkers in a panel distinguishing mild from critical coronavirus disease 2019 outcomes

Laia Reverté  1   2   3 , Elena Yeregui  1   2 , Montserrat Olona  1   2   3   4 , Alicia Gutiérrez-Valencia  5   6 , Maria José Buzón  7 , Anna Martí  1   2 , Frederic Gómez-Bertomeu  1   4 , Teresa Auguet  1   2   4 , Luis F López-Cortés  5   6 , Joaquin Burgos  7 , Clara Benavent-Bofill  1 , Carme Boqué  1   4 , Graciano García-Pardo  1   2   4 , Ezequiel Ruiz-Mateos  5   6 , Maria Teresa Mestre  1 , Francesc Vidal  1   2   3   4 , Consuelo Viladés  1   2   3   4 , Joaquim Peraire  1   2   3   4 , Anna Rull  1   2   3   4 ; COVIDOMICS Study Group
Affiliations

Fetuin-A, inter-α-trypsin inhibitor, glutamic acid and ChoE (18:0) are key biomarkers in a panel distinguishing mild from critical coronavirus disease 2019 outcomes

Laia Reverté et al. Clin Transl Med. 2022 Jan.
No abstract available

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Conflict of interest statement

The authors declare that they have no conflict of interest.

Figures

FIGURE 1
FIGURE 1
Study design and clinical characterization of the study cohort. (A) Flowchart of the clinical strategy followed to categorize patients of the coronavirus disease‐2019 (COVID‐19) study cohort. (B) Incidence of comorbidities (a), COVID‐19 symptoms (b), medication (c), and oxygen & intensive care (d), grouped by disease severity as mild, severe and critical patients. The size of bars (a, c) and circular (b, d) portions is proportional to the percentage of the corresponding comorbidity, symptom, medication or treatment. While patients with mild disease presented mostly anosmia, were treated with antibiotics and did not require oxygen supply, the incidence of dyspnea was significantly higher in the severe and critical groups, many of the latter requiring corticosteroids, hydroxychloroquine, lopinavir/ritonavir. Low‐flow oxygen therapies were mainly necessary for severe patients, some of who required high‐flow oxygen administration and, a high proportion of critical patients were intubated and required vasopressor administration or dialysis. Please note that COVID‐19‐related medication (b) was dispensed after blood sample collection, so that is assumed the subsequent analysis are not biased due to exposure to medication at the time of blood collection
FIGURE 2
FIGURE 2
Serum proteomics profile of coronavirus disease‐2019 (COVID‐19) study cohort. (A) Heatmap showing significant proteins increasing or decreasing in accordance with disease severity. Columns correspond to the degree of disease severity: mild (left), severe (centre) and critical (right) groups. Mean values for each compound in each coronavirus disease‐2019 (COVID‐19) group (columns) are colour‐coded based on relative abundance, low (red) & high (green). Among the 65 significant proteins, 33 increased and 32 decreased with disease severity. (B) Up‐(upper) and down‐(bottom) regulated protein networks sorted by gene‐name showing a tight interconnection within the up‐ and down‐regulated proteins. (C) Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis sorted by pathway impact and ‐log10 (p). The interconnected up‐ and down‐regulated genes were enriched in 77 pathways, and the 10 relevant pathways whose impact values were greater than 0.1 (p < .05) were further considered. The bubble diagram shows matched pathways according to the p‐values and pathway impact values. The size of bubbles shows pathway impact value and the colour denote the level of significance by means of p‐values. Numbers in circles correspond to the significantly enriched pathways ordered from the highest to the lowest pathway impact value. The number of matched proteins, impact value and p‐value corresponding to each pathway are indicated on the inserted table. (D) Random forest analysis showing the 15 protein‐encoding genes ranked by classification accuracy to distinguish between a mild and critical group of patients. Squares on the right represent the COVID‐19 (1 = mild, 2 = severe and 3 = critical) and the colours depict the accuracy power (red and blue for high and low accuracy, respectively)
FIGURE 3
FIGURE 3
Metabolomic and lipidomic profile of coronavirus disease‐2019 (COVID‐19) patients grouped by disease severity. (A) Heatmap plots significant relative abundance of metabolites & lipids increasing or decreasing in accordance with disease severity. Significant differences (p‐values < .05) were determined by ANOVA test followed by post‐hoc Bonferroni correction for mean relative abundance between mild, severe and critical COVID‐19 groups of patients. Columns correspond to the degree of disease severity: mild (left), severe (centre) and critical (right) groups. Mean values for each compound in each COVID‐19 group (columns) are colour‐coded based on relative abundance, low (red) & high (green). (B) Metabolomic and lipidomic Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis and related blood diseases in COVID‐19 patient cohort. Functional metabolic enrichment pathway of 15 metabolites selected from random forest modelling (Fisher's exact test. p < .05) (left), and the corresponding enriched blood pathway diseases (right). (C) Random forest modelling of significant metabolites and lipids with the highest classification accuracy. Right‐legend indicates the capacity of the compounds to differentiate groups of severity, blue (low) and red (high)
FIGURE 4
FIGURE 4
Evaluation of potential biomarkers to be a part of a panel to distinguish coronavirus disease‐2019 (COVID‐19) outcomes. (A) Heatmap showing the Spearman correlation coefficient of pairwise comparison between demographic, clinical and most enriched proteomic, metabolomic and lipidomic biomolecules determined in the blood of patients with mild (1), severe (2) and critical (3) COVID‐19. Spearman matrices are colour‐coded (‐1:1, orange:purple through white) and correlations with p‐values < .05 were considered statistically significant. (B) Receiver operating characteristic (ROC) curves analysis for the predictive power of top selected protein‐encoding genes, lipids and metabolites in random forest analysis to differentiate patients with mild from those with a critical illness. (C) ROC curve analysis for the most enriched proteomic, metabolomic and lipidomic biomolecules to differentiate severe from critically‐ill patients with COVID‐19. (D) Binary logistic regression modelling analysis testing the accuracy of the four selected biomarkers to differentiate mild from critically ill patients with COVID‐19 in a randomly selected set of patients
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