Proteomic Profiles in Patients with Thrombosis Due to COVID-19 Are Distinct from Non-COVID-19 Thrombosis
Bibliographic record
Abstract
Abstract BACKGROUND. COVID-19 is a prothrombotic disease, characterized by endotheliopathy, hypercoagulability, and thromboembolic complications. We hypothesized that the pathogenesis of thromboembolism associated with COVID-19 might differ from thromboembolism in patients without COVID-19. In this study, we sought to evaluate the proteomic signatures of plasma from patients with venous thromboembolism with and without COVID-19. METHODS. Between December 17, 2020 and February 25, 2021 blood was collected from 48 hospitalized patients. Of these 24 had a confirmed diagnosis of COVID-19 infection (COVID+) and radiologic confirmation of arterial or venous thromboembolism (TE+); 17 had COVID-19 infection with absence of arterial thrombosis clinically and absence of venous thromboembolism on lower extremity Doppler ultrasound or chest CT angiography (COVID+/TE-), while 7 were arterial or venous thromboembolism in the absence of COVID-19 (COVID-/TE+). Blood was collected in sodium citrate tubes and centrifuged at 4000 rpm for 20 minutes, with resulting plasma supernatant used for protein profiling performed at Eve Technologies (Calgary, Alberta, Canada). Institutional Review Board approval was obtained for this study. Statistical analysis was performed using GraphPad Prism (v9.1, GraphPad Software, San Diego, CA) and R (v4, R Core Team). P values <0.05 were considered statistically significant. A heatmap was generated using Heatmapper (heatmapper.ca) to represent the concentrations of proteins. RESULTS. The median age was 63 years; overall 25 (52%) were men (13 [54%] among COVID+/TE+, 11 [65%] among COVID+/TE-, and 1 [14%] among COVID-/TE+). In COVID-19 patients who developed thromboembolic events, several proteins associated with inflammation, complement activation, and hemostasis were present at higher levels than in non-COVID-19 patients who developed thromboembolic events (Fig. 1). These included complement factors C2 and C5a, pentraxin-3 (PTX-3), lipocalin-2 (LCN2), resistin (RETN), platelet endothelial cell adhesion molecule-1 (Pecam1), serum amyloid A (SAA), and tissue factor (TF). The heatmap indicates relative protein levels detected in each subject (columns) for proteins (rows) that had statistically significant differences between groups (Fig. 2). Heatmap revealed relatively lower levels of all proteins in patients with thromboembolism without COVID-19 and relatively higher levels of proteins in patients with COVID-19, and especially in ICU patients with COVID-19 and thromboembolism. CONCLUSIONS. Thromboembolic complications in patients with COVID-19 are associated with increased levels of various proteins involved in complement activation and immunothrombotic cascades, compared to thrombotic events in the absence of COVID-19. Activation of the classical complement pathway as evidenced by a relative increase in complement factor C2 may lead to increased TF activation, reflecting more substantial endothelial damage in COVID-19 patients. Higher levels of Pecam1, SAA, LCN2, and RETN all point to increased endotheliopathy, inflammation, and tissue damage in COVID-19 compared to non-COVID-19 thrombosis. These findings may offer insights into novel therapeutic strategies to treat immunothrombotic complications of COVID-19. Figure 1 Figure 1. Disclosures No relevant conflicts of interest to declare.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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) | 0.001 | 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".