Abstract 623: Effect of High-intensity Statin Therapy on High-density Lipoprotein (HDL) Subfractions and Regression of Coronary Atheroma: The SATURN Trial
Bibliographic record
Abstract
Aim: Statin therapy can slow the progression of coronary atherosclerosis. However, the clinical factors underlying the beneficial effects of statin therapy on disease progression remain poorly understood. We examined the relationship of circulating HDL subfractions with measures of coronary atheroma following long-term high-intensity statin therapy. Methods: Serial coronary intravascular ultrasound (IVUS) was utilized in SATURN to monitor changes in atheroma burden [percent atheroma volume (PAV)] in 915 patients with coronary artery disease, treated with rosuvastatin (40 mg) or atorvastatin (80 mg) daily for 24 months. Results: Baseline levels of total HDL-C, apo E-containing HDL-C (apoE HDL-C), HDL 3 -C and HDL 2 -C did not differ significantly between treatment groups. Compared with the atorvastatin-treated group, rosuvastatin-treated patients demonstrated greater increases in levels of total HDL-C (4.4% vs. -1.8%, p<0.001), apoE HDL-C (8.5% vs. -3.3%, p<0.001), HDL 3 -C (3.3% vs. -2.7%, p<0.001) and HDL 2 -C (7.0% vs. -0.7%, p<0.001). The alterations in apo E HDL-C and HDL 3 -C levels were associated with PAV regression in rosuvastatin-treated patients (β=-0.84, p=0.018 and β=-1.10, p=0.056, respectively) but not in the atorvastatin-treated group (β=0.41, p=0.22 and β=0.71, p=0.22 for apo E HDL-C and HDL 3 -C, respectively). Conclusions: Rosuvastatin therapy resulted in favorable changes in HDL subfractions and on-treatment levels of apoE HDL-C and HDL 3 -C were associated with greater disease regression.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".