The plasma metabolome of long COVID-19 patients two years after infection (preprint)

Background: One of the major challenges currently faced by global health systems is the prolonged COVID-19 syndrome (also known as long COVID) which has emerged as a consequence of the SARS-CoV-2 epidemic. The World Health Organization (WHO) recognized long COVID as a distinct clinical entity in 2021. It is estimated that at least 30% of patients who have had COVID-19 will develop long COVID. This has put a tremendous strain on still-overstretched healthcare systems around the world. Methods: In this study, our goal was to assess the plasma metabolome in a total of 108 samples collected from healthy controls, COVID-19 patients, and long COVID patients recruited in Mexico between 2020 and 2022. A targeted metabolomics approach using a combination of LC-MS/MS and FIA MS/MS was performed to quantify 108 metabolites. IL-17 and leptin concentrations were measured in long COVID patients by immunoenzymatic assay. Results: The comparison of paired COVID-19/post-COVID-19 samples revealed 53 metabolites that were statistically different (FDR < 0.05). Compared to controls, 29 metabolites remained dysregulated even after two years. Notably, glucose, kynurenine, and certain acylcarnitines continued to exhibit altered concentrations similar to the COVID-19 phase, while sphingomyelins and long saturated and monounsaturated LysoPCs, phenylalanine, butyric acid, and propionic acid levels normalized. Post-COVID-19 patients displayed a heterogeneous metabolic profile, with some showing no symptoms while others exhibiting a variable number of symptoms. Lactic acid, lactate/pyruvate ratio, ornithine/citrulline ratio, sarcosine, and arginine were identified as the most relevant metabolites for distinguishing patients with more complicated long COVID evolution. Additionally, IL-17 levels were significantly increased in these patients. Conclusions: Mitochondrial dysfunction, redox state imbalance, impaired energy metabolism, and chronic immune dysregulation are likely to be the main hallmarks of long COVID even two years after acute COVID-19 infection.

Targeted Metabolomics Identifies High Performing Diagnostic and Prognostic Biomarkers for COVID-19 (preprint)

Research exploring the development and outcome of COVID-19 infections has led to the need to find better diagnostic and prognostic biomarkers. This cross-sectional study used targeted metabolomics to identify potential COVID-19 biomarkers that predicted the course of the illness by assessing 110 endogenous plasma metabolites from individuals admitted to a local hospital for diagnosis/treatment. Patients were classified into four groups (≈ 40 each) according to standard polymerase chain reaction (PCR) COVID-19 testing and disease course: PCR-/controls (i.e., non-COVID controls), PCR+/not-hospitalized, PCR+/hospitalized, and PCR+/intubated. Blood samples were collected within 2 days of admission/PCR testing. Metabolite concentration data, demographic data and clinical data were used to propose biomarkers and develop optimal regression models for the diagnosis and prognosis of COVID-19. The area under the receiver operating characteristic curve (AUC; 95%CI) was used to assess each models’ predictive value. A panel that included the kynurenine: tryptophan ratio, LysoPC a C26:0, and pyruvic acid discriminated non-COVID controls from PCR+/not-hospitalized (AUC = 0.947; 95%CI 0.931–0.962). A second panel consisting of C10:2, butyric acid, and pyruvic acid distinguished PCR+/not-hospitalized from PCR+/hospitalized and PCR+/intubated (AUC = 0.975; 95%CI 0.968–0.983). Only LysoPC a C28:0 differentiated PCR+/hospitalized from PCR+/intubated patients (AUC = 0.770; 95%CI 0.736–0.803). If additional studies with targeted metabolomics confirm the diagnostic value of these plasma biomarkers, such panels could eventually be of clinical use in medical practice.