Identification of genetic factors associated with susceptibility to angiotensin-converting enzyme inhibitors-induced cough
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: Angiotensin-converting enzyme inhibitors (ACEi) are the first selected drugs for hypertensive patients because of its protective properties against heart and kidney diseases. Persistent cough is a common adverse reaction associated with ACEi, which can bind to the treatment cessation, but its etiology remains an unresolved issue. The most accepted mechanism is that the inhibition of ACEi increases kinins levels, resulting in the activation of proinflammatory mechanisms and nitric oxide generation. However, relatively little is known about the genetic susceptibility to ACEi-induced cough in hypertensive patients. METHODS: We carried out a monogenic association analysis of 39 polymorphisms and haplotypes in genes encoding key proteins related to ACEi activity with the occurrence of ACEi-induced cough. We also carried out a digenic association analysis and investigated the existence of epistatic interactions between the analyzed polymorphisms using a logistic regression procedure. Finally, we investigated the predictive value of the identified associations for ACEi-induced cough. RESULTS: We found that genetic polymorphisms in MME [rs2016848, P=0.002, odds ratio (OR)=1.795], BDKRB2 (rs8012552, P=0.012, OR=1.609), PTGER3 (rs11209716, P=0.002, OR=0.565), and ACE (rs4344) genes are associated with ACEi-related cough. For the latter, the effect is sex specific, having a protective effect in males (P=0.027, OR=0.560) and increasing the risk in females (P=0.031, OR=1.847). In addition, genetic interactions between peptidases involved in kinins levels (CPN1 and XPNPEP1) and proteins related to prostaglandin metabolism (PTGIS and PTGIR) strongly modify the risk of ACEi-induced cough presentation (0.102≤OR≤0.384 for protective combinations and 2.732≤OR≤7.216 for risk combinations). CONCLUSION: These results are consistent with the hypothesis that the mechanism of cough is related to the accumulation of bradykinin, substance P, and prostaglandins.
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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 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".