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Longitudinal Metabolomics of Human Plasma Reveals Robust Prognostic Markers of COVID-19 Disease Severity.

ABSTRACT: There is an urgent need to identify which COVID-19 patients will develop life-threatening illness so that scarce medical resources can be optimally allocated and rapid treatment can be administered early in the disease course, when clinical management is most effective. To aid in the prognostic classification of disease severity, we performed untargeted metabolomics profiling of 341 patients with plasma samples collected at six longitudinal time points. Using the temporal metabolic profiles and machine learning, we then built a predictive model of disease severity. We determined that the levels of 25 metabolites measured at the time of hospital admission successfully predict future disease severity. Through analysis of longitudinal samples, we confirmed that these prognostic markers are directly related to disease progression and that their levels are restored to baseline upon disease recovery. Finally, we validated that these metabolites are also altered in a hamster model of COVID-19. Our results indicate that metabolic changes associated with COVID-19 severity can be effectively used to stratify patients and inform resource allocation during the pandemic.

SUBMITTER: Sindelar M

PROVIDER: S-EPMC7872388 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Longitudinal metabolomics of human plasma reveals prognostic markers of COVID-19 disease severity.

Project description:There is an urgent need to identify which COVID-19 patients will develop life-threatening illness so that medical resources can be optimally allocated and rapid treatment can be administered early in the disease course, when clinical management is most effective. To aid in the prognostic classification of disease severity, we perform untargeted metabolomics on plasma from 339 patients, with samples collected at six longitudinal time points. Using the temporal metabolic profiles and machine learning, we build a predictive model of disease severity. We discover that a panel of metabolites measured at the time of study entry successfully determines disease severity. Through analysis of longitudinal samples, we confirm that most of these markers are directly related to disease progression and that their levels return to baseline upon disease recovery. Finally, we validate that these metabolites are also altered in a hamster model of COVID-19.

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Longitudinal Metabolomics of Human Plasma Reveals Robust Prognostic Markers of COVID-19 Disease Severity (part I)

Project description:There is an urgent need to identify which COVID-19 patients will develop life-threatening illness so that medical resources can be optimally allocated and rapid treatment can be administered early in the disease course, when clinical management is most effective. To aid in the prognostic classification of disease severity, we perform untargeted metabolomics on plasma from 339 patients, with samples collected at six longitudinal time points. Using the temporal metabolic profiles and machine learning, we build a predictive model of disease severity. We discover that a panel of metabolites measured at the time of study entry successfully determine disease severity. Through analysis of longitudinal samples, we confirm that the majority of these markers are directly related to disease progression and that their levels are restored to baseline upon disease recovery. Finally, we validate that these metabolites are also altered in a hamster model of COVID-19. Our results indicate that metabolic changes associated with COVID-19 severity can be effectively used to stratify patients and inform resource allocation during the pandemic.

2021-01-29 | ST001849 | MetabolomicsWorkbench

Longitudinal Metabolomics of Human Plasma Reveals Robust Prognostic Markers of COVID-19 Disease Severity (Part 2)

Project description:There is an urgent need to identify which COVID-19 patients will develop life-threatening illness so that medical resources can be optimally allocated and rapid treatment can be administered early in the disease course, when clinical management is most effective. To aid in the prognostic classification of disease severity, we perform untargeted metabolomics on plasma from 339 patients, with samples collected at six longitudinal time points. Using the temporal metabolic profiles and machine learning, we build a predictive model of disease severity. We discover that a panel of metabolites measured at the time of study entry successfully determine disease severity. Through analysis of longitudinal samples, we confirm that the majority of these markers are directly related to disease progression and that their levels are restored to baseline upon disease recovery. Finally, we validate that these metabolites are also altered in a hamster model of COVID-19. Our results indicate that metabolic changes associated with COVID-19 severity can be effectively used to stratify patients and inform resource allocation during the pandemic.

2021-01-28 | ST001853 | MetabolomicsWorkbench

Untargeted metabolomics of COVID-19 patient serum reveals potential prognostic markers of both severity and outcome.

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Longitudinal proteomic profiling discovered early prognostic and surrogate markers in COVID-19

Project description:In managing patients with coronavirus disease 2019 (COVID-19), early identification of those at high risk and real-time monitoring of disease progression to severe COVID-19 is a major challenge. We aimed to identify early prognostic protein markers and to discover surrogate protein markers that effectively reflect the clinical progression of the disease. We performed in-depth proteome profiling on 137 sera, longitudinally collected from 25 patients with COVID-19 (non-severe patients, n = 13; patients who progressed to severe COVID-19, n = 12). We identified 11 potential biomarkers, including the novel markers IGLV3-19 and BNC2, as early prognostic indicators of severe COVID-19. These potential biomarkers are mainly involved in biological processes associated with humoral immune response, interferon signalling, acute phase response, lipid metabolism, and platelet degranulation. We further revealed that the longitudinal changes of 40 proteins persistently increased or decreased as the disease progressed to severe COVID-19. These 40 potential biomarkers could effectively reflect the clinical progression of the disease. This study supports the development of protein biomarkers, which might enable better predicting and monitoring progression to severe COVID-19.

2021-09-10 | PXD023686 | Pride

Plasma markers of COVID-19 severity: a pilot study.

Project description:BackgroundSARS-CoV-2 infected patients show heterogeneous clinical presentations ranging from mild symptoms to severe respiratory failure and death. Consequently, various markers reflect this wide spectrum of disease presentations.MethodsOur pilot cohort included moderate (n = 10) and severe (n = 10) COVID-19 patients, and 10 healthy controls. We determined plasma levels of nine acute phase proteins (APPs) by nephelometry, and full-length (M65), caspase-cleaved (M30) cytokeratin 18, and ADAMTS13 (a disintegrin-like and metalloprotease with thrombospondin type-1 motif 13) by ELISA. In addition, we examined whole plasma N-glycosylation by capillary gel electrophoresis coupled to laser-induced fluorescence detection (CGE-LIF).ResultsWhen compared to controls, COVID-19 patients had significantly lower concentrations of ADAMTS13 and albumin (ALB) but higher M30, M65, α1-acid glycoprotein (AGP), α1-antitrypsin (AAT), ceruloplasmin (CP), haptoglobin (HP), and high-sensitivity C-reactive protein (hs-CRP). The concentrations of α1-antichymotrypsin (ACT), α2-macroglobulin (A2MG) and serum amyloid A (SAA) proteins did not differ. We found significantly higher levels of AAT and M65 but lower ALB in severe compared to moderate COVID-19 patients. N-glycan analysis of the serum proteome revealed increased levels of oligomannose- and sialylated di-antennary glycans and decreased non-sialylated di-antennary glycan A2G2 in COVID-19 patients compared to controls.ConclusionsCOVID-19-associated changes in levels and N-glycosylation of specific plasma proteins highlight complexity of inflammatory process and grant further investigations.

| S-EPMC9745704 | biostudies-literature

Longitudinal proteomic profiling of dialysis patients with COVID-19 reveals markers of severity and predictors of death.

Project description:End-stage kidney disease (ESKD) patients are at high risk of severe COVID-19. We measured 436 circulating proteins in serial blood samples from hospitalised and non-hospitalised ESKD patients with COVID-19 (n = 256 samples from 55 patients). Comparison to 51 non-infected patients revealed 221 differentially expressed proteins, with consistent results in a separate subcohort of 46 COVID-19 patients. Two hundred and three proteins were associated with clinical severity, including IL6, markers of monocyte recruitment (e.g. CCL2, CCL7), neutrophil activation (e.g. proteinase-3), and epithelial injury (e.g. KRT19). Machine-learning identified predictors of severity including IL18BP, CTSD, GDF15, and KRT19. Survival analysis with joint models revealed 69 predictors of death. Longitudinal modelling with linear mixed models uncovered 32 proteins displaying different temporal profiles in severe versus non-severe disease, including integrins and adhesion molecules. These data implicate epithelial damage, innate immune activation, and leucocyte-endothelial interactions in the pathology of severe COVID-19 and provide a resource for identifying drug targets.

| S-EPMC8064756 | biostudies-literature

Longitudinal Plasma Proteomics Analysis of Acute COVID-19

Project description:The host response to COVID-19 pathophysiology over the first days of infection remains largely unclear especially the mechanisms in the blood compartment. We report here on a longitudinal proteomic analysis of acute phase COVID-19 patients, for which we used blood plasma and MRM proteomics with internal standards as well as DIA. We measured samples on admission for 49 patients, of which 21 with additional samples on days 2, 4, 7, and 14 after admission. We also measured 30 externally obtained samples from healthy individuals for comparison at baseline.

2022-02-16 | PXD029437 | Pride

Salivary miRNA profiles in COVID-19 patients with different disease severity [Plasma]

Project description:The oral mucosa is the first site of SARS-CoV-2 entry and replication, and it plays a central role in the early defense against infection. Thus, SARS-CoV-2 viral load, miRNAs, cytokines, and neutralizing activity (NA) were assessed in saliva and plasma from mild (MD) and severe (SD) COVID-19 patients. Here we show that of the 84 miRNAs analysed, 8 are differently express in plasma and saliva of SD. In particular: 1) miRNAs let-7a-5p, let-7b-5p, let-7c-5p are significantly downregulated; and 2) miR-23a and b, miR-29c, as well as three immunomodulatory miRNAs (miR-34a-5p, miR-181d-5p, miR-146) are significantly upregulated. The production of pro-inflammatory cytokines (IL-1β, IL-2, IL-6, IL-8, IL-9 and TNFα) and chemokines (CCL2 and RANTES) increase in both saliva and plasma of SD and MD. Notably, disease severity correlates with NA and immune activation. Monitoring these parameters could help to predict disease outcome and identify new markers of disease progression.

2023-07-19 | GSE235978 | GEO

The longitudinal characterization of immune responses in COVID-19 patients reveals novel prognostic signatures for disease severity, patients' survival and long COVID.

Project description:IntroductionSARS-CoV-2 pandemic still poses a significant burden on global health and economy, especially for symptoms persisting beyond the acute disease. COVID-19 manifests with various degrees of severity and the identification of early biomarkers capable of stratifying patient based on risk of progression could allow tailored treatments.MethodsWe longitudinally analyzed 67 patients, classified according to a WHO ordinal scale as having Mild, Moderate, or Severe COVID-19. Peripheral blood samples were prospectively collected at hospital admission and during a 6-month follow-up after discharge. Several subsets and markers of the innate and adaptive immunity were monitored as putative factors associated with COVID-19 symptoms.ResultsMore than 50 immunological parameters were associated with disease severity. A decision tree including the main clinical, laboratory, and biological variables at admission identified low NK-cell precursors and CD14+CD91+ monocytes, and high CD8+ Effector Memory T cell frequencies as the most robust immunological correlates of COVID-19 severity and reduced survival. Moreover, low regulatory B-cell frequency at one month was associated with the susceptibility to develop long COVID at six months, likely due to their immunomodulatory ability.DiscussionThese results highlight the profound perturbation of the immune response during COVID-19. The evaluation of specific innate and adaptive immune-cell subsets allows to distinguish between different acute and persistent COVID-19 symptoms.

| S-EPMC11317765 | biostudies-literature

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