Meta-Analysis of COVID-19 Metabolomics Identifies Variations in Robustness of Biomarkers
Ontology highlight
ABSTRACT:
SUBMITTER: Onoja A
PROVIDER: S-EPMC10531504 | biostudies-literature | 2023 Sep
REPOSITORIES: biostudies-literature
Similar Datasets
Project description: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.
| S-EPMC8290000 | biostudies-literature
Project description:Cystic fibrosis (CF) lung disease is characterized by infection, inflammation, lung function decline, and intermittent pulmonary exacerbations. However, the link between pulmonary exacerbation and lung disease progression remains unclear. Global metabolomic profiling can provide novel mechanistic insight into a disease process in addition to putative biomarkers for future study. Our objective was to investigate how the plasma metabolomic profile changes between CF pulmonary exacerbation and a clinically well state.Plasma samples and lung function data were collected from 25 CF patients during hospitalization for a pulmonary exacerbation and during quarterly outpatient clinic visits. In collaboration with Metabolon, Inc., the metabolomic profiles of matched pair plasma samples, one during exacerbation and one at a clinic visit, were analyzed using gas and liquid chromatography coupled with mass spectrometry. Compounds were identified by comparison to a library of standards. Mixed effects models that controlled for nutritional status and lung function were used to test for differences and principal components analysis was performed.Our population had a median age of 27 years (14-39) and had a median FEV1 % predicted of 65% (23-105%). 398 total metabolites were identified and after adjustment for confounders, five metabolites signifying perturbations in nucleotide (hypoxanthine), nucleoside (N4-acetylcytidine), amino acid (N-acetylmethionine), carbohydrate (mannose), and steroid (cortisol) metabolism were identified. Principal components analysis provided good separation between the two clinical phenotypes.Our findings provide putative metabolite biomarkers for future study and allow for hypothesis generation about the pathophysiology of CF pulmonary exacerbation.
| S-EPMC5553866 | biostudies-other
Project description:Recently, numerous studies have reported on different predictive models of disease severity in COVID-19 patients. Herein, we propose a highly predictive model of disease severity by integrating routine laboratory findings and plasma metabolites including cytosine as a potential biomarker of COVID-19 disease severity. One model was developed and internally validated on the basis of ROC-AUC values. The predictive accuracy of the model was 0.996 (95% CI: 0.989 to 1.000) with an optimal cut-off risk score of 3 from among 6 biomarkers including five lab findings (D-dimer, ferritin, neutrophil counts, Hp, and sTfR) and one metabolite (cytosine). The model is of high predictive power, needs a small number of variables that can be acquired at minimal cost and effort, and can be applied independent of non-empirical clinical data. The metabolomics profiling data and the modeling work stemming from it, as presented here, could further explain the cause of COVID-19 disease prognosis and patient management.
| S-EPMC10414581 | biostudies-literature
Project description:The novel coronavirus SARS-CoV-2 has spread across the world since 2019, causing a global pandemic. The pathogenesis of the viral infection and the associated clinical presentations depend primarily on host factors such as age and immunity, rather than the viral load or its genetic variations. A growing number of omics studies have been conducted to characterize the host immune and metabolic responses underlying the disease progression. Meta-analyses of these datasets have great potential to identify robust molecular signatures to inform clinical care and to facilitate therapeutics development. In this study, we performed a comprehensive meta-analysis of publicly available global metabolomics datasets obtained from three countries (United States, China and Brazil). To overcome high heterogeneity inherent in these datasets, we have (a) implemented a computational pipeline to perform consistent raw spectra processing; (b) conducted meta-analyses at pathway levels instead of individual feature levels; and (c) performed visual data mining on consistent patterns of change between disease severities for individual studies. Our analyses have yielded several key metabolic signatures characterizing disease progression and clinical outcomes. Their biological interpretations were discussed within the context of the current literature. To the best of our knowledge, this is the first comprehensive meta-analysis of global metabolomics datasets of COVID-19.
| S-EPMC7827862 | biostudies-literature
Project description:We have witnessed the 2-year-long global rampage of COVID-19 caused by the wide spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). However, knowledge about biomarkers of the entire COVID-19 process is limited. Identification of the systemic features of COVID-19 will lead to critical biomarkers and therapeutic targets for early intervention and clinical disease course prediction. Here, we performed a comprehensive analysis of clinical measurements and serum metabolomics in 199 patients with different stages of COVID-19. In particular, our study is the first serum metabolomic analysis of critical rehabilitation patients and critical death patients. We found many differential metabolites in the comparison of metabolomic results between ordinary, severe, and critical patients and uninfected patients. Through the metabolomic results of COVID-19 patients in various stages, and critical rehabilitation patients and critical death patients, we identified a series of differential metabolites as biomarkers, a separate queue and precise distinction, and predicted COVID-19 verification. These differentially expressed metabolites, included 1,2-di-(9Z,12Z-octadecadienoyl)-sn-glycero-3-phosphate, propylparaben, 20-hydroxyeicosatetraenoic acid, triethanolamine, chavicol, disialosyl galactosyl globoside, 1-arachidonoylglycerophosphoinositol, and alpha-methylstyrene, all of which have been identified for the first time as biomarkers in COVID-19 progression. These biomarkers are involved in many pathological and physiological pathways of COVID-19, for example, immune responses, platelet degranulation, and metabolism which might result in pathogenesis. Our results showed valuable information about metabolites obviously altered in COVID-19 patients with different stages, which could shed light on the pathogenesis as well as serve as potential therapeutic agents of COVID-19.
| S-EPMC9108257 | biostudies-literature
Project description:SARS-CoV-2 infection remains a major public health concern, particularly for the aged and those individuals with co-morbidities at risk for developing severe COVID-19. Understanding the pathogenesis and biomarkers associated with responses to SARS-CoV-2 infection remain critical components in developing effective therapeutic approaches, especially in cases of severe and long-COVID-19. In this study blood plasma protein expression was compared in subjects with mild, moderate, and severe COVID-19 disease. Evaluation of an inflammatory protein panel confirms upregulation of proteins including TNFβ, IL-6, IL-8, IL-12, already associated with severe cytokine storm and progression to severe COVID-19. Importantly, we identify several proteins not yet associated with COVID-19 disease, including mesothelin (MSLN), that are expressed at significantly higher levels in severe COVID-19 subjects. In addition, we find a subset of markers associated with T-cell and dendritic cell responses to viral infection that are significantly higher in mild cases and decrease in expression as severity of COVID-19 increases, suggesting that an immediate and effective activation of T-cells is critical in modulating disease progression. Together, our findings identify new targets for further investigation as therapeutic approaches for the treatment of SARS-CoV-2 infection and prevention of complications of severe COVID-19.
| S-EPMC10755324 | biostudies-literature
Project description:BackgroundAccurate detection of SARS-CoV-2 is necessary to mitigate the COVID-19 pandemic. However, the test reagents and assay platforms are varied and may not be sufficiently robust to diagnose COVID-19.MethodsWe reviewed 85 studies (21 530 patients), published from five regions of the world, to highlight issues involved in the diagnosis of COVID-19 in the early phase of the pandemic. All relevant articles, published up to 31 May 2020, in PubMed, BioRiXv, MedRiXv and Google Scholar, were included. We evaluated the qualitative (9749 patients) and quantitative (10 355 patients) performance of RT-PCR and serologic diagnostic tests for real-world samples, and assessed the concordance (5538 patients) between test performance in meta-analyses. Synthesis of results was done using random effects modelling and bias was evaluated according to QUADAS-2 guidelines.ResultsThe RT-PCR tests exhibited heterogeneity in the primers and reagents used. Of 1957 positive RT-PCR COVID-19 participants, 1585 had positive serum antibody (IgM±IgG) tests (sensitivity 0.81, 95% CI 0.66 to 0.90). While 3509 of 3581 participants RT-PCR negative for COVID-19 were found negative by serology testing (specificity 0.98, 95% CI 0.94 to 0.99). The chemiluminescent immunoassay exhibited the highest sensitivity, followed by ELISA and lateral flow immunoassays. Serology tests had higher sensitivity and specificity for laboratory approval than for real-world reporting data.DiscussionThe robustness of the assays/platforms is influenced by variability in sampling and reagents. Serological testing complements and may minimise false negative RT-PCR results. Lack of standardised assay protocols in the early phase of pandemic might have contributed to the spread of COVID-19.
| S-EPMC9023849 | biostudies-literature
Project description:ObjectiveTo systematically investigate the relationship between cardiac biomarkers and COVID-19 severity and mortality.MethodsWe performed a literature search using PubMed, Web of Science, and Google Scholar. The standardized mean difference (SMD) and 95% confidence interval (CI) were applied to estimate the combined results of 67 studies. A meta-analysis of cardiac biomarkers was used to evaluate disease mortality and severity in COVID-19 patients.ResultsA meta-analysis of 7812 patients revealed that patients with high levels of cardiac troponin I (SMD = 0.81 U/L, 95% CI = 0.14-1.48, P = 0.017), cardiac troponin T (SMD = 0.78 U/L, 95% CI = 0.07-1.49, P = 0.032), high-sensitive cardiac troponin I (SMD = 0.66 pg/mL, 95% CI = 0.51-0.81, P < 0.001), high-sensitive cardiac troponin T (SMD = 0.93 U/L, 95% CI = 0.21-1.65, P = 0.012), creatine kinase-MB (SMD = 0.54 U/L, 95% CI = 0.39-0.69, P < 0.001), and myoglobin (SMD = 0.80 U/L, 95% CI = 0.57-1.03, P < 0.001) were associated with prominent disease severity in COVID-19 infection. Moreover, 9532 patients with a higher serum level of cardiac troponin I (SMD = 0.51 U/L, 95% CI = 0.37-0.64, P < 0.001), high-sensitive cardiac troponin (SMD = 0.51 ng/L, 95% CI = 0.29-0.73, P < 0.001), high-sensitive cardiac troponin I (SMD = 0.51 pg/mL, 95% CI = 0.38-0.63, P < 0.001), high-sensitive cardiac troponin T (SMD = 0.85 U/L, 95% CI = 0.63-1.07, P < 0.001), creatine kinase-MB (SMD = 0.48 U/L, 95% CI = 0.32-0.65, P < 0.001), and myoglobin (SMD = 0.55 U/L, 95% CI = 0.45-0.65, P < 0.001) exhibited a prominent level of mortality from COVID-19 infection.ConclusionCardiac biomarkers (cardiac troponin I, cardiac troponin T, high-sensitive cardiac troponin, high-sensitive cardiac troponin I, high-sensitive cardiac troponin T, creatine kinase-MB, and myoglobin) should be more frequently applied in identifying high-risk COVID-19 patients so that timely treatment can be implemented to reduce severity and mortality in COVID-19 patients.
| S-EPMC8320426 | biostudies-literature
Project description:BackgroundVaccines play a crucial role in the response to COVID-19 and their efficacy is thus of great importance.AimTo assess the robustness of COVID-19 vaccine efficacy (VE) trial results using the fragility index (FI) and fragility quotient (FQ) methodology.MethodsWe conducted a Cochrane and PRISMA-compliant systematic review and meta-analysis of COVID-19 VE trials published worldwide until 22 January 2023. We calculated the FI and FQ for all included studies and assessed their associations with selected trial characteristics using Wilcoxon rank sum tests and Kruskal-Wallis H tests. Spearman correlation coefficients and scatter plots were used to quantify the strength of correlation of FIs and FQs with trial characteristics.ResultsOf 6,032 screened records, we included 40 trials with 54 primary outcomes, comprising 909,404 participants with a median sample size per outcome of 13,993 (interquartile range (IQR): 8,534-25,519). The median FI and FQ was 62 (IQR: 22-123) and 0.50% (IQR: 0.24-0.92), respectively. FIs were positively associated with sample size (p < 0.001), and FQs were positively associated with type of blinding (p = 0.023). The Spearman correlation coefficient for FI with sample size was moderately strong (0.607), and weakly positive for FI and FQ with VE (0.138 and 0.161, respectively).ConclusionsThis was the largest study on trial robustness to date. Robustness of COVID-19 VE trials increased with sample size and varied considerably across several other important trial characteristics. The FI and FQ are valuable complementary parameters for the interpretation of trial results and should be reported alongside established trial outcome measures.
| S-EPMC10236928 | biostudies-literature
Project description:The ongoing Coronavirus Disease 2019 (COVID-19) pandemic is caused by the highly infectious Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2). There is an urgent need for biomarkers that will help in better stratification of patients and contribute to personalized treatments. We performed targeted proteomics using the Olink platform and systematically investigated protein concentrations in 350 hospitalized COVID-19 patients, 186 post-COVID-19 individuals, and 61 healthy individuals from 3 independent cohorts. Results revealed a signature of acute SARS-CoV-2 infection, which is represented by inflammatory biomarkers, chemokines and complement-related factors. Furthermore, the circulating proteome is still significantly affected in post-COVID-19 samples several weeks after infection. Post-COVID-19 individuals are characterized by upregulation of mediators of the tumor necrosis (TNF)-α signaling pathways and proteins related to transforming growth factor (TGF)-ß. In addition, the circulating proteome is able to differentiate between patients with different COVID-19 disease severities, and is associated with the time after infection. These results provide important insights into changes induced by SARS-CoV-2 infection at the proteomic level by integrating several cohorts to obtain a large disease spectrum, including variation in disease severity and time after infection. These findings could guide the development of host-directed therapy in COVID-19.
| S-EPMC9670186 | biostudies-literature