Renin-Angiotensin System Genes Polymorphisms in Patients With COVID-19 and Its Relation to Severe Cases of SARS-CoV-2 Infection
Bibliographic record
Abstract
Background: Different variants of single nucleotide polymorphisms (SNPs) of angiotensinogen (AGT), angiotensin-converting enzyme type 1 (ACE1), and angiotensin II receptors type 1 (AGTR1) and 2 (AGTR2) genes determine different susceptibility to cardiovascular disease (CVD) and hypertension, which can be considered as risk factors for fatal outcomes among coronavirus disease 2019 (COVID-19) patients. The objective of our study was to assess the relation between the frequency of SNPs of the renin-angiotensin system (RAS) components, and the severity of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Methods: ) ≤ 93%, signs of unstable hemodynamics with systolic blood pressure (SBP) < 90 and/or diastolic blood pressure (DBP) < 60 mm Hg. All patients were identified with alleles and genotypes of the polymorphic markers rs4762 of the AGT gene, rs1799752 of the ACE1 gene, rs5186 of the AGTR1 gene and rs1403543 of the AGTR2 gene using the polymerase chain reaction method in human DNA preparations on real-time CFX96C1000 Touch, Bio-Rad equipment (Syntol, Russia). Statistical analysis was performed in R v.4.2. Results: Patients were divided into groups with severe (n = 44) and moderate COVID-19 (n = 56). For ACE1 rs1799752, a significant deviation from the population distribution was detected in both studied subgroups. A higher frequency of the C allele SNP rs5186 AGTR1 gene was detected in the group with severe disease. More frequent A/A genotype of SNP rs1403543 AGTR2 was detected among females with severe COVID-19. Haplotype analysis revealed more common DCG haplotype among patients with severe COVID-19. The odds ratio for severe COVID-19 in the presence of the DCG haplotype was 3.996 (95% confidential interval: 1.080 -14.791, P < 0.05). Conclusions: Our data suggest that the SNP genes of the RAS components, may allow to identify groups of patients predisposed to a more severe course of COVID-19.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".