Proteomics validate circulating GDF-15 as an independent biomarker for COVID-19 severity
Bibliographic record
Abstract
Introduction: Growth differentiation factor 15 (GDF-15) was originally described as a stress-induced cytokine, and a biomarker of aging and cardiovascular diseases. We hypothesized that circulating GDF-15 would be associated with COVID-19 disease severity. Herein, we explored this hypothesis in a large cohort of COVID-19 patients. Methods: Blood samples were collected from 926 COVID-19 adult patients and from 285 hospitalized controls from the Biobanque Québécoise de la COVID-19 (BQC19). COVID-19 severity was graded according to the WHO criteria. SOMAscan proteomics assay was performed on 50µL of plasma. ELISA were performed on 46 selected participants with left-over plasma to validate differences in plasma GDF-15 levels. Statistical analyses were conducted using GraphPad Prism 9.0 and SPSS. P values < 0.01 were considered significant. Results: Proteomics showed that plasma GDF-15 levels were higher in COVID-19 patients compared to hospitalized controls. GDF-15 levels increased with COVID-19 severity. COVID-19 patients presenting with comorbidities including diabetes, cancer, chronic obstructive pulmonary disease (COPD) and cardiovascular disease had higher GDF-15 levels. ELISA revealed significant elevation of GDF-15 until 30 days after hospitalization. Plasma GDF-15 elevation was correlated with older age. Moreover, GDF-15 levels correlated with pro-inflammatory cytokine interleukin-6 (IL-6) and inflammation marker C-reactive protein (CRP) as well as soluble levels of its putative receptor CD48. No association was established between anti-SARS-CoV-2 IgG levels and plasma GDF-15 levels. Conclusions: This study confirms GDF-15 as a biomarker for COVID-19 severity. Clinical evaluation of GDF-15 levels could assist identification of persons at high-risk of progressing to severe disease, thus improving patient care.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".