Angiotensin-converting enzyme 2 (ACE2) levels in relation to risk factors for COVID-19 in two large cohorts of patients with atrial fibrillation
Bibliographic record
Abstract
AIMS: The global COVID-19 pandemic is caused by the SARS-CoV-2 virus entering human cells using angiotensin-converting enzyme 2 (ACE2) as a cell surface receptor. ACE2 is shed to the circulation, and a higher plasma level of soluble ACE2 (sACE2) might reflect a higher cellular expression of ACE2. The present study explored the associations between sACE2 and clinical factors, cardiovascular biomarkers, and genetic variability. METHODS AND RESULTS: Plasma and DNA samples were obtained from two international cohorts of elderly patients with atrial fibrillation (n = 3999 and n = 1088). The sACE2 protein level was measured by the Olink Proteomics® Multiplex CVD II96 × 96 panel. Levels of the biomarkers high-sensitive cardiac troponin T (hs-cTnT), N-terminal probrain natriuretic peptide (NT-proBNP), growth differentiation factor 15 (GDF-15), C-reactive protein, interleukin-6, D-dimer, and cystatin-C were determined by immunoassays. Genome-wide association studies were performed by Illumina chips. Higher levels of sACE2 were statistically significantly associated with male sex, cardiovascular disease, diabetes, and older age. The sACE2 level was most strongly associated with the levels of GDF-15, NT-proBNP, and hs-cTnT. When adjusting for these biomarkers, only male sex remained associated with sACE2. We found no statistically significant genetic regulation of the sACE2 level. CONCLUSIONS: Male sex and clinical or biomarker indicators of biological ageing, cardiovascular disease, and diabetes are associated with higher sACE2 levels. The levels of GDF-15 and NT-proBNP, which are associated both with the sACE2 level and a higher risk for mortality and cardiovascular disease, might contribute to better identification of risk for severe COVID-19 infection.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".