Dabigatran etexilate and reduction in serum apolipoprotein B
Bibliographic record
Abstract
OBJECTIVE: Carboxylesterases, which convert dabigatran etexilate to its active form, dabigatran, have also been shown to influence lipoprotein metabolism, although any pleotropic effects of the drug based on this possible mechanism has not been evaluated. We examined the effects of dabigatran etexilate on serum lipoprotein markers in the Randomized Evaluation of Long-Term Anticoagulation Therapy (RE-LY) study. METHODS: 2513 participants from the RE-LY randomised control trial with baseline and 3-month apolipoprotein B (ApoB) and apolipoprotein A1 (ApoA1) measurements were included. We prospectively compared the effects of dabigatran 110 mg twice daily, dabigatran 150 mg twice daily and warfarin on changes in ApoB and ApoA1 concentrations using a mixed model analysis. RESULTS: From baseline to 3 months, a significant reduction in ApoB concentration was observed with low-dose dabigatran (-0.057 (95% CI -0.069 to -0.044) g/L, p<0.001) and high-dose dabigatran (-0.065 (95% CI -0.078 to -0.053) g/L, p<0.001) but not warfarin (-0.006 g/L (95% CI -0.018 to 0.007) g/L, p=0.40). Compared with warfarin, ApoB reduction was significantly greater with both doses of dabigatran (p<0.001 for both groups). Reductions in ApoA1 concentrations did not statistically differ with either dose of dabigatran when compared with warfarin. CONCLUSIONS: Dabigatran is associated with a significant (∼7%) reduction in ApoB concentration, suggesting a novel effect of this drug on lipoprotein metabolism. Further studies are needed to determine the mechanism of this observed effect, and its impact on clinical outcomes.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".