Aspirin for Primary Cardiovascular Prevention in Patients with Diabetes: Uncertainties and Opportunities
Bibliographic record
Abstract
The use of the antiplatelet agent aspirin (acetylsalicylic acid) was previously routinely recommended for the primary prevention of cardiovascular (CV) events in patients with diabetes, but recent large-scale randomized trials have failed to demonstrate a sizeable net clinical benefit with a once-daily, low-dose (81-100 mg) regimen in this population. Previous pharmacokinetic and pharmacodynamic studies have suggested that the aspirin formulation (enteric-coated) and dosing schedule (once daily) studied in randomized trials for primary prevention of CV events defining contemporary clinical practice may not leverage the full potential of the drug, particularly in patients with diabetes. Indeed, the diabetic platelets bear characteristics that increase their thrombotic potential and alter their pharmacologic response to the drug. Consequently, the appropriateness of studying a uniform aspirin regimen in landmark primary prevention trials needs to be revisited. In this review, we present the evidence showing that diabetes not only increases baseline platelet reactivity, but also alters platelet response to aspirin through different mechanisms including a faster platelet turnover rate. Obesity, which is frequently associated with diabetes, also impacts its pharmacokinetics via an increase in distribution volume. Small-scale pharmacokinetic and pharmacodynamic studies have suggested that the relative aspirin resistance phenotype observed in patients with diabetes may be reversed with a twice-daily dosing schedule, and with nonenteric-coated aspirin formulations. Properly powered randomized controlled trials investigating the efficacy and safety of aspirin dosing schedules and formulations tailored to the population of patients with diabetes are urgently required to optimize patient care.
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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 | 0.025 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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".