Sex Differences in Clopidogrel Effects Among Young Patients With Acute Coronary Syndrome: A Role for Genetics?
Bibliographic record
Abstract
Background Poorer health outcomes experienced by young women with acute coronary syndrome may be related to sex differences in the safety and efficacy of antiplatelet agents, such as clopidogrel. Polymorphisms in drug metabolism enzyme (cytochrome P450 [ CYP ] family) genes are independent factors for the variability in response to clopidogrel. However, a sex-specific impact of genetics to explain worse clinical outcomes in women has not been explored extensively. Therefore, our objective was to determine whether an interaction of sex with CYP variants occurs among users of clopidogrel, and if so, its impact on 1-year adverse clinical outcomes. Methods We used data from a combined cohort of 2272 patients (median age 49 years; 56% female) hospitalized for acute coronary syndrome. We examined interactions between sex and CYP variants among clopidogrel users at admission and discharge to assess associations with 1-year readmission due to cardiac events. Results The case-only analysis of 177 participants on clopidogrel at the time of presentation showed that the risk of an atherothrombotic event was greater in female carriers of the CYP2C9∗3 loss-of-function allele (odds ratio = 3.77, 95% confidence interval = 1.54-9.24). The results of the multivariable logistic regression model for users of clopidogrel at discharge (n = 1733) indicated that women had significantly higher risk of atherothrombotic readmissions at 1 year (odds ratio = 1.55, 95% confidence interval = 1.16-2.07), compared to the risk for men, but the loss-of-function alleles, either individually or through a genetic risk score, were not associated with 1-year readmissions. Conclusion This study highlights the need for an improved understanding of the role of sex-by-gene interactions in causing sex differences in drug metabolism.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".