Comprehensive clinical and metabolomics profiling of COVID-19 Mexican patients across three epidemiological waves
Bibliographic record
Abstract
Introduction: As of mid-2024, COVID-19 has affected over 676 million people worldwide, leading to more than 6.8 million deaths. Numerous studies have documented metabolic changes occurring during both the acute phase of the disease and the recovery phase, which, in some cases, contribute to the development of long COVID syndrome. Aims and methods: In this study, we aimed to evaluate clinical, laboratory, and comprehensive metabolomic data from hospitalized COVID-19 patients during the second, third and fourth waves (Alpha, Delta, and Omicron). A targeted, fully quantitative metabolomics assay (TMIC MEGA Assay) was used to measure 529 metabolites and lipids in plasma samples. The metabolomic profiles of these patients were compared according to different and relevant factors impacting COVID-19 outcome, such as age, sex, comorbidities, and vaccination status. Results: Among the 21 classes of compounds evaluated in this study, amino acids and lipids were the most dysregulated when comparing age, sex, comorbidities, vaccination status, and the different epidemiological waves. This is the most comprehensive analysis in Mexico providing absolute quantitative data for 529 metabolites and lipids measured in hospitalized COVID-19 patients, which could be used to monitor their metabolic status and clinical outcomes associated with COVID-19 infection or with long COVID syndrome.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".