P1769Lowering the neutrophil to lymphocyte ratio by the BET inhibitor, apabetalone: potential implications for cardiovascular events in high risk patients
Bibliographic record
Abstract
Background: In addition to traditional inflammatory markers, the neutrophil to lymphocyte ratio (NLR) has been identified as a marker of systemic inflammation. Higher NLR has been associated with adverse clinical outcomes and is predictive of incident events in patients with CVD, diabetes and CKD. Apabetalone selectively inhibits the second ligand domain in bromodomain and extra terminal (BET) proteins, which are epigenetic readers of acetylated lysine marks on histone tails. Apabetalone modifies inflammatory pathways implicated in vascular disease and reduces incidence of major adverse cardiovascular events (MACE: death, non-fatal myocardial infarction and hospitalization for cardiovascular causes) in pooled data from phase 2 studies (SUSTAIN & ASSURE, n=499). Purpose: To evaluate the impact of apabetalone treatment on the NLR and its association with MACE. Methods: Neutrophil and lymphocyte counts were collected in the haematology panels during two phase 2 trials: SUSTAIN and ASSURE, which compared the effects of treatment with apabetalone 200 mg bid (n=331) and placebo (n=168) for up to 26 weeks on circulating cardiovascular biomarkers and atherosclerotic plaque in patients with established CVD (n=499).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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) | 0.002 | 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".