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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".