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Targeted metabolomics identifies high performing diagnostic and prognostic biomarkers for COVID-19.

ABSTRACT: 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.

SUBMITTER: Lopez-Hernandez Y

PROVIDER: S-EPMC8290000 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature

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Targeted metabolomics identifies high performing diagnostic and prognostic biomarkers for COVID-19.

López-Hernández Yamilé Y Monárrez-Espino Joel J Oostdam Ana-Sofía Herrera-van AH Delgado Julio Enrique Castañeda JEC Zhang Lun L Zheng Jiamin J Valdez Juan José Oropeza JJO Mandal Rupasri R González Fátima de Lourdes Ochoa FLO Moreno Juan Carlos Borrego JCB Trejo-Medinilla Flor M FM López Jesús Adrián JA Moreno José Antonio Enciso JAE Wishart David S DS

Scientific reports 20210719 1

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 ...[more]

PMID: 34282210

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Project description:BackgroundCOVID-19 is primarily considered a respiratory tract infection, but it can also affect the central nervous system (CNS), which can result in long-term sequelae. In contrast to CNS infections by classic neurotropic viruses, SARS-CoV-2 is usually not detected in cerebrospinal fluid (CSF) from patients with COVID-19 with neurological involvement (neuro-COVID), suggesting fundamental differences in pathogenesis.MethodsTo assess differences in CNS metabolism in neuro-COVID compared to CNS infections with classic neurotropic viruses, we applied a targeted metabolomic analysis of 630 metabolites to CSF from patients with (i) COVID-19 with neurological involvement [n = 16, comprising acute (n = 13) and post-COVID-19 (n = 3)], (ii) viral meningitis, encephalitis, or myelitis (n = 10) due to herpes simplex virus (n = 2), varicella zoster virus (n = 6), enterovirus (n = 1) and tick-borne encephalitis virus (n = 1), and (iii) aseptic neuroinflammation (meningitis, encephalitis, or myelitis) of unknown etiology (n = 21) as additional disease controls.ResultsStandard CSF parameters indicated absent or low neuroinflammation in neuro-COVID. Indeed, CSF cell count was low in neuro-COVID (median 1 cell/µL, range 0-12) and discriminated it accurately from viral CNS infections (AUC = 0.99) and aseptic neuroinflammation (AUC = 0.98). 32 CSF metabolites passed quality assessment and were included in the analysis. Concentrations of differentially abundant (fold change ≥|1.5|, FDR ≤ 0.05) metabolites were both higher (9 and 5 metabolites) and lower (2 metabolites) in neuro-COVID than in the other two groups. Concentrations of citrulline, ceramide (d18:1/18:0), and methionine were most significantly elevated in neuro-COVID. Remarkably, triglyceride TG(20:1_32:3) was much lower (mean fold change = 0.09 and 0.11) in neuro-COVID than in all viral CNS infections and most aseptic neuroinflammation samples, identifying it as highly accurate biomarker with AUC = 1 and 0.93, respectively. Across all samples, TG(20:1_32:3) concentration correlated only moderately with CSF cell count (ρ = 0.65), protein concentration (ρ = 0.64), and Q-albumin (ρ = 0.48), suggesting that its low levels in neuro-COVID CSF are only partially explained by less pronounced neuroinflammation.ConclusionsThe results suggest that CNS metabolite responses in neuro-COVID differ fundamentally from viral CNS infections and aseptic neuroinflammation and may be used to discover accurate diagnostic biomarkers in CSF and to gain insights into differences in pathophysiology between neuro-COVID, viral CNS infections and aseptic neuroinflammation.

Dataset Information

Targeted metabolomics identifies high performing diagnostic and prognostic biomarkers for COVID-19.

Publications

Targeted metabolomics identifies high performing diagnostic and prognostic biomarkers for COVID-19.

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