Metabolomic Insights into COVID-19 Severity: A Scoping Review
Bibliographic record
Abstract
BACKGROUND: In 2019, SARS-CoV-2, the novel coronavirus, entered the world scene, presenting a global health crisis with a broad spectrum of clinical manifestations. Recognizing the significance of metabolomics as the omics closest to symptomatology, it has become a useful tool for predicting clinical outcomes. Several metabolomic studies have indicated variations in the metabolome corresponding to different disease severities, highlighting the potential of metabolomics to unravel crucial insights into the pathophysiology of SARS-CoV-2 infection. METHODS: The PRISMA guidelines were followed for this scoping review. Three major scientific databases were searched: PubMed, the Directory of Open Access Journals (DOAJ), and BioMed Central, from 2020 to 2024. Initially, 2938 articles were identified and vetted with specific inclusion and exclusion criteria. Of these, 42 articles were retrieved for analysis and summary. RESULTS: Metabolites were identified that were repeatedly noted to change with COVID-19 and its severity. Phenylalanine, glucose, and glutamic acid increased with severity, while tryptophan, proline, and glutamine decreased, highlighting their association with COVID-19 severity. Additionally, pathway analysis revealed that phenylalanine, tyrosine and tryptophan biosynthesis, and arginine biosynthesis were the most significantly impacted pathways in COVID-19 severity. CONCLUSIONS: COVID-19 severity is intricately linked to significant metabolic alterations that span amino acid metabolism, energy production, immune response modulation, and redox balance.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.017 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".