Relationship between cholesteryl ester transfer protein and LDL heterogeneity in familial hypercholesterolemia
Bibliographic record
Abstract
Small, dense LDL particles have been associated with an increased risk of coronary artery disease, and cholesteryl ester transfer protein (CETP) has been suggested to play a role in LDL particle remodeling. We examined the relationship between LDL heterogeneity and plasma CETP mass in familial hypercholesterolemia (FH). LDL particles were characterized by polyacrylamide gradient gel electrophoresis in a total of 259 FH heterozygotes and 208 nonFH controls. CETP mass was measured by enzyme-linked immunosorbent assay in a subgroup of 240 participants, which included 120 FH patients matched with 120 controls. As compared with controls, FH subjects had an 11% higher CETP mass. Moreover, LDL-peak particle diameter (LDL-PPD) was significantly smaller in FH heterozygotes than in controls (258.1 +/- 4.8 vs. 259.2 +/- 4.1 A; P = 0.01) after adjustment for covariates. There was also an inverse relationship between LDL-PPD and CETP mass (R = -0.15; P = 0.02), and this relationship was abolished by adjustment for the FH/control status, indicating that LDL-PPD changes in FH are mediated, at least in part, by an increase in plasma CETP mass concentrations. These results suggest that increased plasma CETP mass concentrations could lead to significant LDL particle remodeling in FH heterozygotes and could contribute to the pathogenesis of atherosclerosis.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".