Comparative plasma metabolomics of Delta and Omicron SARS-CoV-2 variants: insights into variant-specific pathogenesis and therapeutic implications
Bibliographic record
Abstract
Background: The emergence of SARS-CoV-2 led to a global pandemic. Delta and Omicron, classified as concerning variants, differ significantly in transmissibility, disease severity, and antibody neutralization. Delta is associated with more severe disease, whereas Omicron is linked to increased transmissibility yet milder disease. This study investigates plasma metabolomic differences between Delta and Omicron infections and their associations with disease severity and treatment response. Importantly, this work examines variant-specific treatment metabolic effects - an aspect that remains underexplored despite the ongoing evolution of SARS-CoV-2 variants - and thus begins to fill a critical gap in the literature. Methods: A total of 109 hospitalized SARS-CoV-2 patients, confirmed by RT-PCR positivity (53 Delta, 56 Omicron), were matched by age and sex. Plasma samples collected on hospitalization days 1, 2, and 7 were analyzed using DI/LC-MS/MS-based (direct injection, liquid chromatography-tandem mass-spectrometry) targeted metabolomics. We employed univariate and multivariate statistical and pathway analyses to investigate and characterize metabolomic differences. Results: Distinct metabolic profiles differentiated Delta and Omicron infections. Specific metabolites, including tyrosine, asparagine, leucine, and acylcarnitines (C3, C4, C5), significantly distinguished variants and severity groups. Delta infections showed higher associations with severe outcomes. Corticosteroid treatment influenced metabolic profiles, revealing associations with modulation of metabolic and clinical responses. Conclusion: This study reveals significant plasma-based metabolic differences between Delta and Omicron SARS-CoV-2 variants, potentially reflecting their distinct clinical outcomes and severities.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".