Plasma Proteome of Long-covid Patients Indicates Hypoxia-mediated Vasculo-proliferative Disease With Impact on Brain and Heart Function
Bibliographic record
Abstract
Abstract Aims Long-COVID occurs after SARS-CoV-2 infection and results in diverse, prolonged symptoms. The present study aims to determine the underlying mechanisms, and to inform prognosis and treatment. Methods Plasma proteome from Long-COVID outpatients was analyzed in comparison to acutely ill COVID-19 (mild and severe) inpatients and healthy control subjects. The expression of approximately 3000 protein biomarkers was determined with proximity extension assays and then deconvoluted with multiple bioinformatics tools into both cell types and signaling mechanisms, as well as organ specificity. Results Compared to age- and sex-matched acutely ill COVID-19 inpatients and healthy control subjects, Long-COVID outpatients showed natural killer cells with a resting phenotype, as opposed to active, and neutrophils that formed extracellular traps. This resetting of cell phenotypes was reflected in vascular events mediated by both angiopoietin-1 (ANGPT1) and vascular-endothelial growth factor-A (VEGFA). Levels of ANGPT1 and VEGFA were validated by serological methods in different patient cohorts. Silent signaling of transforming growth factor-β1 with elevated EP300 favored not only vascular inflammation, but also tumor necrosis factor-α driven pathways. In addition, a vascular proliferative state associated with hypoxia inducible factor 1 pathway was predicted that progressed from COVID-19 to Long-COVID. The vasculo-proliferative process identified in Long-COVID was associated with significant changes in the organ-specific proteome reflective of neurological and cardiometabolic dysfunction. Conclusions Taken together, our study uncovered a vasculo-proliferative process in Long-COVID initiated by prior hypoxia, and identified potential organ-specific prognostic biomarkers and therapeutic targets.
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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 | 0.000 |
| 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".