Bempedoic acid lowers high-sensitivity C-reactive protein and low-density lipoprotein cholesterol: Analysis of pooled data from four phase 3 clinical trials
Bibliographic record
Abstract
BACKGROUND AND AIMS: High-sensitivity C-reactive protein (hsCRP), a marker for atherosclerotic cardiovascular disease risk, is reduced by bempedoic acid. We assessed the relationship between changes in low-density lipoprotein cholesterol (LDL-C) and hsCRP in relation to baseline statin use. METHODS: Pooled data from four phase 3 trials (patients on maximally tolerated statins [Pool 1] and patients receiving no or low-dose statins [Pool 2]) were used to determine the proportion of patients with baseline hsCRP ≥2 mg/L who achieved hsCRP <2 mg/L at week 12. The percentage of patients who achieved hsCRP <2 mg/L and guideline-recommended LDL-C (Pool 1, <70 mg/dL; Pool 2, <100 mg/dL) was determined for patients on statins in Pool 1 and those not on statins in Pool 2, as was the correlation between percent changes in hsCRP and LDL-C. RESULTS: Overall, 38.7% in Pool 1 and 40.7% in Pool 2 with baseline hsCRP ≥2 mg/L achieved hsCRP <2 mg/L with bempedoic acid, with little effect from background statin. Among patients taking a statin in Pool 1 or not taking a statin in Pool 2, 68.6% and 62.4% achieved hsCRP <2 mg/L. Both hsCRP <2 mg/L and United States guideline-recommended LDL-C were achieved more often with bempedoic acid vs. placebo (20.8% vs. 4.3%, respectively, in Pool 1 and 32.0% vs. 5.3%, in Pool 2). Changes in hsCRP and LDL-C were only weakly correlated (Pool 1, r = 0.112; Pool 2, r = 0.173). CONCLUSIONS: Bempedoic acid significantly reduced hsCRP irrespective of background statin therapy; the effect was largely independent of LDL-C lowering.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| 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) | 0.000 | 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 teacher head, 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".