Levels of Angiotensin and Kinin Metabolite Peptides Related to COVID-19 Severity
Bibliographic record
Abstract
In addition to crucial roles in normal human biology, peptide metabolites of the renin-angiotensin (RAS) and kallikrein-kinin systems (KKS) have been reported to be altered in COVID-19 patients. Here, we evaluate new data on RAS and KKS peptides in COVID-19 patient serum obtained from a recently developed, fully validated, and optimized stable isotope labeling LC-MS peptide assay. We found that the RAS peptides angiotensin (ANG) 1, 2, 1-5, and 1-7 were downregulated compared to COVID-free surrogate controls, while the KKS peptides Brad, Brad 1-8, and Brad 1-7 were upregulated. This paper focuses on uncovering the possible diagnostic value of these peptides using receiver operating characteristic (ROC) analyses of these data. ROC plots confirmed that all of the analyte peptides in 80 serum samples from COVID-19 patients were significantly altered from "normal" values of the control samples. The best diagnostic sensitivities and selectivities for COVID vs no COVID were found in ROC plots for Brad and Brad 1-7 (both 99% sensitivity, 100% selectivity). We then analyzed levels of all the peptides grouped according to preassigned values of the World Health Organization (WHO) COVID-19 Severity Index. ROC plots differentiated patients with a high WHO severity index from those with a low WHO severity index with moderate success, with BRAD (73% sensitivity, 79% selectivity) and Ang 1-7 (75% sensitivity, 65% selectivity) giving the best diagnostic performance. Results suggest the possible diagnostic value of these peptides as biomarkers to help identify moderate and serious COVID-19 cases at relatively early stages.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".