Longitudinal NMR-Based Metabolomics Study Reveals How Hospitalized COVID-19 Patients Recover: Evidence of Dyslipidemia and Energy Metabolism Dysregulation

Abstract

Long COVID, or post-acute sequelae of SARS-CoV-2 infection (PASC), can manifest as long-term symptoms in multiple organ systems, including respiratory, cardiovascular, neurological, and metabolic systems. In patients with severe COVID-19, immune dysregulation is significant, and the relationship between metabolic regulation and immune response is of great interest in determining the pathophysiological mechanisms. We aimed to characterize the metabolomic footprint of recovering severe COVID-19 patients at three consecutive timepoints and compare metabolite levels to controls. Our findings add proof of dysregulated amino acid metabolism in the acute phase and dyslipidemia, glycoprotein level alterations, and energy metabolism disturbances in severe COVID-19 patients 3-4 months post-hospitalization.

Keywords: COVID-19; SARS-CoV-2; dyslipidemia; metabolomics; nuclear magnetic resonance; post-COVID-19 condition; post-COVID-19 syndrome; post-acute sequelae of SARS-CoV-2 infection.

Figure 1

(A) Study design. The study cohort consists of 41 hospitalized COVID-19 patients, for whom samples were collected at three timepoints: (1) acute phase (Timepoint A) on the 1st or 2nd day of hospitalization; (2) recovery phase (Timepoint B) 35  ±  14.7.65 days later; and (3) later recovery phase (Timepoint C) 99  ±  16.79 days after the first sample. (B) Venn diagram showing the count of statistically significant metabolites between timepoints A, B, and C. (C) Top 50 significantly changed features (metabolites) based on limma linear regression analysis for time series data visualized in the heatmap; each row conforms to a specific metabolite expressed in a normalized, log-transformed concentration value, each column represents one sample, and the samples are arranged by timepoint. Distance measure: euclidean; clustering algorithm: ward. (D) Time-course profiles for the top 3 features (by Hotelling’s T² value) from multivariate empirical Bayes statistical time-series analysis (MEBA); each line represents one patient.

Figure 2

Timepoint A vs. population controls. (A) Volcano plot visualizing the distribution of metabolites in the analyzed contrast, significance (−log₁₀(p-value)) versus log₂ fold change is plotted on the y and x axes, respectively, resulting in 13 strongly significant metabolites (fold change > 1.5 and FDR < 0.05) indicated in red in the plot. (B) Scatterplot representing the most relevant metabolic pathways from the KEGG library, arranged by adjusted p values (obtained by Global Test pathway enrichment analysis) on the y-axis and pathway impact values (from pathway topology analysis) on the x-axis. The node color is based on its p value, and the node radius is determined based on pathway impact values. (C) Timepoint A metabolites (x axis) vs. Timepoint B blood tests (y axis) in COVID-19 patients. Heatmap with the most clinically relevant metabolite (Timepoint A) correlations with biochemical and hematological analysis results from the clinical lab at recovery phase Timepoint B (~month after admission at the hospital), color represents the strength of the relationship and its direct or inverse nature; only the pairs with sufficient data and significant correlations (p-value > 0.05) are shown. (D) Timepoint A metabolites (x axis) vs. Timepoint C blood tests (y axis). Heatmap visualizing metabolite (acute phase) correlations with biochemical and hematological analysis results in recovery phase Timepoint C (~month after admission at the hospital), color represents the strength of the relationship and its direct or inverse nature; only the pairs with sufficient data and significant correlations (p-value > 0.05) are shown.

Figure 3

Timepoint B vs. population controls. (A) Volcano plot visualizing the distribution of metabolites in the analyzed contrast, significance (−log₁₀(p-value)) versus log₂ fold change is plotted on the y and x axes, respectively. (B) Scatterplot representing the most relevant metabolic pathways from the KEGG library, arranged by adjusted p values (obtained by Global Test pathway enrichment analysis) on the y-axis and pathway impact values (from pathway topology analysis) on the x-axis. The node color is based on its p value, and the node radius is determined based on pathway impact values. (C) Timepoint B metabolites (x axis) vs. Timepoint B blood tests (y axis) in COVID-19 patients. Heatmap visualizing statistically significant metabolite correlations with biochemical and hematological analysis results in recovery phase Timepoint B (~month after admission at the hospital), color represents the strength of the relationship and its direct or inverse nature; only the pairs with sufficient data and significant correlations (p-value > 0.05) are shown.

Figure 4

Timepoint C vs. population controls. (A) Volcano plot visualizing the distribution of metabolites in the analyzed contrast, significance (−log₁₀(p-value)) versus log₂ fold change is plotted on the y and x axes, respectively. (B) Scatterplot representing the most relevant metabolic pathways from the KEGG library, arranged by adjusted p values (obtained by Global Test pathway enrichment analysis) on the y-axis and pathway impact values (from pathway topology analysis) on the x-axis. The node color is based on its p value, and the node radius is determined based on pathway impact values. (C) Timepoint C metabolites (x axis) vs. Timepoint C blood tests (y axis) in COVID-19 patients. Heatmap visualizing statistically significant metabolite correlations with biochemical and hematological analysis results in recovery phase Timepoint B (~month after admission at the hospital), color represents the strength of the relationship and its direct or inverse nature; only the pairs with sufficient data and significant correlations (p-value > 0.05) are shown.