Relationships Between Low-Density Lipoprotein Particle Size, Plasma Lipoproteins, and Progression of Coronary Artery Disease
Bibliographic record
Abstract
BACKGROUND: The Diabetes Atherosclerosis Intervention Study showed that treatment with fenofibrate decreases progression of coronary atherosclerosis in subjects with type 2 diabetes. We determined whether on-treatment plasma lipid concentrations and LDL particle size contribute to the favorable effect of fenofibrate on the progression of coronary artery disease (CAD). METHODS AND RESULTS: A total of 418 subjects with type 2 diabetes were randomly assigned to 200 mg micronized fenofibrate daily or placebo. The mean follow-up time was 39.6 months. LDL peak particle diameter (LDL size) was determined by polyacrylamide gradient gel electrophoresis from 405 subjects at baseline and at the end of the study. Progression of CAD was measured with quantitative coronary angiography. LDL size increased significantly more in the fenofibrate group than in the placebo group (0.98+/-1.04 versus 0.32+/-0.92 nm, P<0.001). In the combined group, small LDL size was significantly associated with progression of CAD measured as the increase of percentage diameter stenosis (r=-0.16, P=0.002) and decreases in minimum (r=-0.11, P=0.030) and mean (r=-0.10, P=0.045) lumen diameter. High on-treatment LDL cholesterol, apolipoprotein B, and triglyceride concentrations were also associated with the progression of CAD. In regression analyses, small LDL size added to the effect of LDL cholesterol and apolipoprotein B on the progression of CAD. Similar associations were observed in the fenofibrate group, whereas in the placebo group, lipoprotein variables were not significantly correlated with the progression of CAD. CONCLUSIONS: Changes in LDL size and plasma lipid levels account for part of the antiatherogenic effect of fenofibrate in type 2 diabetes.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.001 | 0.001 |
| 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".