Apabetalone lowers serum alkaline phosphatase and improves cardiovascular risk in patients with cardiovascular disease
Bibliographic record
Abstract
BACKGROUND AND AIMS: In patients with cardiovascular disease, considerable residual risk remains despite evidence-based secondary prevention measures. Alkaline phosphatase (ALP) has been suggested as a modifiable cardiovascular risk factor. We sought to determine whether cardiovascular risk reduction by the bromodomain and extra-terminal (BET) protein inhibitor apabetalone is associated with the concomitant lowering of serum ALP. METHODS: In a post-hoc analysis of 795 patients with established coronary heart disease and statin treatment, who participated in phase 2 placebo-controlled trials of apabetalone, we determined the effect of assigned treatment for up to 24 weeks on the incidence of major adverse cardiovascular events (MACE) and serum ALP. RESULTS: Baseline ALP (median 72 U/L) predicted MACE (death, non-fatal myocardial infarction, coronary revascularization, or hospitalization for cardiovascular causes), independent of high-sensitivity C-reactive protein (hsCRP), sex, age, race, study, cardiovascular risk factors, chronic kidney disease (CKD), liver function markers and treatment allocation (hazard ratio [HR] per standard deviation [SD] 1.6, 95% CI 1.19-2.16, p = 0.002). Mean placebo-corrected decreases in ALP from baseline were 9.2% (p < 0.001) after 12-14 weeks and 7.7% (p < 0.001) after 24-26 weeks of apabetalone treatment. In the apabetalone group, a 1-SD reduction in ALP was associated with a HR for MACE of 0.64 (95% CI 0.46-0.90, p = 0.009). CONCLUSIONS: Serum ALP predicts residual cardiovascular risk, independent of hsCRP, established cardiovascular risk factors and CKD, in patients with cardiovascular disease on statin treatment. Apabetalone lowers serum ALP, which was associated with a lower risk of cardiovascular events. Whether the beneficial cardiovascular effects of apabetalone are causally related to ALP reduction remains undetermined.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".