Coping With Stress: The Mitokine GDF-15 as a Biomarker of COVID-19 Severity
Bibliographic record
Abstract
Growth differentiation factor 15 (GDF-15) is a transforming growth factor (TGF)-β superfamily cytokine that plays a central role in metabolism regulation. Produced in response to mitochondrial stress, tissue damage or hypoxia, this cytokine has emerged as one of the strongest predictors of disease severity during inflammatory conditions, cancers and infections. Reports suggest that GDF-15 plays a tissue protective role via sympathetic and metabolic adaptation in the context of mitochondrial damage, although the exact mechanisms involved remain uncertain. In this review, we discuss the emergence of GDF-15 as a distinctive marker of viral infection severity, especially in the context of COVID-19. We will critically review the role of GDF-15 as an inflammation-induced mediator of disease tolerance, through metabolic and immune reprogramming. Finally, we discuss potential mechanisms of GDF-15 elevation during COVID-19 cytokine storm and its limitations. Altogether, this cytokine seems to be involved in disease tolerance to viral infections including SARS-CoV-2, paving the way for novel therapeutic interventions.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".