TO001EFFECTS OF THE BET-INHIBITOR APABETALONE ON CARDIOVASCULAR EVENTS IN PATIENTS WITH TYPE 2 DIABETES MELLITUS AND ACUTE CORONARY SYNDROME, ACCORDING TO PRESENCE OR ABSENCE OF CHRONIC KIDNEY DISEASE. A BETONMACE TRIAL REPORT
Bibliographic record
Abstract
Abstract Background and Aims Patients with type 2 diabetes (T2D) and acute coronary syndrome (ACS) are at high risk for recurrent cardiovascular (CV) events, particularly in the presence of chronic kidney disease (CKD). Apabetalone (APB) is a novel inhibitor of bromodomain and extraterminal (BET) proteins. Its cardiovascular efficacy and safety were evaluated in a phase 3 trial, BETonMACE. Method BETonMACE was a randomized, double-blind, comparison of effects of ABP or placebo (PBO) on major adverse CV events (MACE) defined as CV-death, non-fatal myocardial infarct or stroke, in 2425 pts with T2D and recent ACS. Here we report MACE plus CHF hospitalization in subjects with or without CKD Stage 3. Results Baseline characteristics: median age 62 years, 25.6% female, 87.6% white, 90% high intensity statin use, mean LDL-C 70.3 and HDL-C 33.3 mg/dl, median HbA1c 7.3%, and 11% with CKD Stage 3. Overall in the trial, MACE plus CHF hospitalization occurred in 139 (11.5%) patients with ABP and 173 (14.3%) with PBO (HR 0.78, 95% CI 0.63-0.98). In the subgroup with CKD, MACE plus CHF hospitalization occurred in 16 (12.9%) on APB and 41 (25%) on PBO (HR 0.48, 95% CI 0.26-0.89). In the subgroup without CKD, MACE plus CHF hospitalization occurred in 123 (11.3%) and 132 (12.7%) with APB or PBO, respectively (HR 0.89, 95% CI 0.70-1. Conclusion Patients with T2D, ACS, and Stage 3 CKD have a very high risk of subsequent MACE plus CHF hospitalization. The BET protein inhibitor ABP may reduce this risk.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".