Favorable cognitive effects of the BET protein inhibitor apabetalone in patients 70 and older
Bibliographic record
Abstract
Abstract Background Cognitive decline in late life including Alzheimer’s disease (AD) and vascular dementia (VaD) may be caused by epigenetic change. Bromodomain and extra‐terminal (BET) proteins are epigenetic transcriptional “readers” found to contribute to chronic disease. Apabetalone, a small molecule BET protein inhibitor for oral administration, was assessed for therapeutic effects on cognitive performance in a randomized trial of patients at high risk for cardiovascular disease (CVD). Method In the BETonMACE post‐Acute Coronary Syndrome trial in type 2 diabetes mellitus patients were randomized to apabetalone capsule 100 mg b.i.d. or placebo (n=2425). The Montreal Cognitive Assessment (MoCA) was performed on all patients 70 years or older at baseline (n=464) and yearly in the embedded cognition study. In a prespecified analysis, participants were assigned to one of three groups: MoCA score ≥ 26 (normal performance), MoCA score 25 – 22 (mild cognitive impairment), and MoCA score ≤ 21 (dementia). Result Apabetalone exposure was equivalent in each of the three MoCA‐score defined groups. Apabetalone treatment over approximately two years was associated with an increased total MoCA score in participants with baseline MoCA score of ≤ 21 (p = 0.02). Onset of cognition benefit appeared after 12 months treatment. There was no significant difference in change from baseline in the treatment groups with higher MoCA scores. Conclusion In the BETonMACE trial epigenetic BET protein inhibition by apabetalone capsule 100 mg b.i.d. vs placebo was associated with improved cognition as measured by MoCA in patients with baseline scores of < 21. It is feasible to embed cognition assessment in randomized Phase 3 CVD endpoint studies. BET protein inhibitors warrant further investigation for late life cognitive disorders.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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".