Association between angiotensinogen (AGT), angiotensin-converting enzyme (ACE) and angiotensin-II receptor 1 (AGTR1) polymorphisms and COVID-19 infection in the southeast of Iran: a preliminary case-control study
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic remains an emerging public health crisis with serious adverse effects. The disease is caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV--2) infection, targeting angiotensin-converting enzyme-2 (ACE2) receptor for cell entry. However, changes in the renin-angiotensin system (RAS) balance alter an individual's susceptibility to COVID-19 infection. We aimed to evaluate the association between AGT rs699 C > T, ACE rs4646994 I/D, and AGTR1 rs5186 C > A variants and the risk of COVID-19 infection and the severity in a sample of the southeast Iranian population. METHODS: A total of 504 subjects, including 258 COVID-19 positives, and 246 healthy controls, were recruited. Genotyping of the ACE gene rs4646994, and AGT rs699, and AGTR1 rs5186 polymorphisms was performed by polymerase chain reaction (PCR) and PCR-restriction fragment length polymorphism (PCR-RFLP), respectively. RESULTS: Our results showed that the II genotype of ACE rs4646994 and the I allele decreased the risk of COVID-19 infection. Moreover, we found that the TC genotype and C allele of AGT rs699 increased the risk of COVID-19 infection. The AGTR1 rs5186 was not associated with COVID-19 infection. Also, we did not find any association between these polymorphisms and the severity of the disease. However, we found a significantly higher age and prevalence of diabetes and hypertension in patients with severe disease than a non-severe disease. CONCLUSIONS: These findings suggest that ACE rs4646994 and AGT rs699 polymorphisms increase the risk of COVID-19 infection in a southeast Iranian population.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".