19. Association between Genotype Variation with COVID-19 Severity in Hypertensive Patients: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background: Genetic variations in ACE and ACE2 genes influence COVID-19 severity. Reduced ACE2 availability and increased ACE activity can elevate Angiotensin-II (AngII), contributing to COVID-19-related inflammation. Objective: We aim to assess the relationship between genetic variations and the severity of COVID-19 in individuals with hypertension. Method: We followed the 2020 PRISMA guidelines for reporting. We searched Cochrane Library, ProQuest, PubMed, LILACS, ScienceDirect, Springer, and Taylor & Francis from inception to September 24, 2023. Eligible studies included adults (≥18 years) with COVID-19 and hypertension (SBP ≥ 140 and/or DBP ≥ 90 mmHg), employing observational designs, providing data on genetic variations in ACE1, ACE2, or relevant polymorphisms. Methodological quality was evaluated using the Newcastle–Ottawa scale (NOS). Meta-analysis was conducted using RevMan 5.4.1. Result: We found three case-control studies and one cross-sectional study, generating eight reports in total. The pooled analysis of severe COVID-19 in hypertensive patients with ACE1 polymorphism revealed significant associations in the allele (OR: 2.52; 95%CI, 1.31-4.83; p=0.005), additive (OR: 4.30; 95%CI, 1.04-17.73; p=0.04), and dominant (OR: 2.65; 95%CI, 1.41-4.97; p=0.002) models. Conversely, a protective role against severe COVID-19 in hypertensive patients was indicated in the recessive model (OR: 0.24; 95%CI, 0.07-0.87; p=0.03). Other results yieled no significant association with COVID-19 severity. NOS score for case-controls are 6-8 and cross-sectional are 10. Conclusion: Our analysis linked ACE1 I/D polymorphism to severe COVID-19 in hypertensive individuals, with no significant relationship observed for ACE2 polymorphism. Additional research is needed given the limited relevant studies.
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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 | 0.019 | 0.043 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.045 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".