ArticleMetabolites2021
Comprehensive Meta-Analysis of COVID-19 Global Metabolomics Datasets.
Article in Metabolites, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 54 papers, 3 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
54 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Pooled it
- Amanida: an R package for meta-analysis of metabolomics non-integral data.Bioinformatics (Oxford, England) · 2022Pooled it
- Systematic review with meta-analysis of diagnostic test accuracy for COVID-19 by mass spectrometry.Metabolism: clinical and experimental · 2022Pooled it
- Alteration of the Gut-Lung Axis After Severe COVID-19 Infection and Modulation Through Probiotics: A Randomized, Controlled Pilot Study.Nutrients · 2024Trial
- Benchtop NMR as a clinical tool for acute respiratory illness: metabolic signatures associated with disease severity.Respiratory research · 2026Article
- A longitudinal comparative analysis of serum metabolomic signatures in children with SARS-CoV-2 infection and MIS-C.Communications medicine · 2026Article
- Repurposing the memory-promoting meclofenoxate hydrochloride as a treatment for Parkinson's disease through integrative multi-omics analysis.NPJ Parkinson's disease · 2025Article
- COVID-19: A Systematic Review of Metabolomics Data and Predicting Potential Biomarkers Based on Pathway Analysis.Tanaffos · 2025Review
- Comprehensive clinical and metabolomics profiling of COVID-19 Mexican patients across three epidemiological waves.Frontiers in molecular biosciences · 2025Article
- Metabolomics analysis reveals resembling metabolites between humanized γδ TCR mice and human plasma.Scientific reports · 2024Article
- Novel COVID-19 biomarkers identified through multi-omics data analysis: N-acetyl-4-O-acetylneuraminic acid, N-acetyl-L-alanine, N-acetyltriptophan, palmitoylcarnitine, and glycerol 1-myristate.Internal and emergency medicine · 2024Article
- Metabolic predictors of COVID-19 mortality and severity: a survival analysis.Frontiers in immunology · 2024Article
- Exploring metabolic anomalies in COVID-19 and post-COVID-19: a machine learning approach with explainable artificial intelligence.Frontiers in molecular biosciences · 2024Article
- Systemic perturbations in amino acids/amino acid derivatives and tryptophan pathway metabolites associated with murine influenza A virus infection.Virology journal · 2023Article
- Article
- An Innate Checkpoint Determines Immune Dysregulation and Immunopathology during Pulmonary Murine Coronavirus Infection.Journal of immunology (Baltimore, Md. : 1950) · 2023Article
- Diagnostic, Prognostic and Mechanistic Biomarkers of COVID-19 Identified by Mass Spectrometric Metabolomics.Metabolites · 2023Review
- Label-Free SERS for Rapid Differentiation of SARS-CoV-2-Induced Serum Metabolic Profiles in Non-Hospitalized Adults.Analytical chemistry · 2023Article
- Eliminating host-guest incompatibility via enzyme mining enables the high-temperature production ofiScience · 2023Article
- Integrated metabolomics and lipidomics analyses suggest the temperature-dependent lipid desaturation promotes aflatoxin biosynthesis inFrontiers in microbiology · 2023Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.