Bempedoic acid in patients with type 2 diabetes mellitus, prediabetes, and normoglycaemia: A post hoc analysis of efficacy and glycaemic control using pooled data from phase 3 clinical trials
Bibliographic record
Abstract
AIM: To evaluate the effect of bempedoic acid on glycaemic and lipid variables in patients with hypercholesterolaemia. METHODS: A patient-level pooled analysis of four phase 3, randomized, double-blind, placebo-controlled trials evaluated changes in glycaemia, change from baseline in LDL-C, and adverse events. Patients (N = 3621) on maximally tolerated statins were randomized 2:1 to oral bempedoic acid 180 mg or placebo once daily for 12 to 52 weeks with the results analysed by baseline glycaemic status (diabetes, prediabetes, or normoglycaemia). RESULTS: The annual rate of new-onset diabetes for bempedoic acid versus placebo in patients with normoglycaemia at baseline (n = 618) was 0.3% versus 0.8%, and for patients with prediabetes at baseline (n = 1868) it was 4.7% versus 5.9%. In patients with diabetes or prediabetes, bempedoic acid significantly (P < .0001) reduced HbA1c by -0.12% and -0.06%, respectively, and did not worsen fasting glucose versus placebo. Bempedoic acid significantly and consistently lowered LDL-C levels versus placebo, regardless of baseline glycaemic status (placebo-corrected difference range, -17.2% to -29.6%; P < .001 for each stratum). The safety of bempedoic acid was comparable with placebo and similar across glycaemic strata. CONCLUSIONS: Bempedoic acid significantly lowered LDL-C across glycaemic strata and did not worsen glycaemic variables or increase the incidence of new-onset diabetes versus placebo over a median follow-up of 1 year.
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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 | 0.017 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.013 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".