Cholesterol esterification and atherogenic index of plasma correlate with lipoprotein size and findings on coronary angiography
Bibliographic record
Abstract
We examined the association between rate of cholesterol esterification in plasma depleted of apolipoprotein B-containing lipoproteins (FER(HDL)), atherogenic index of plasma (AIP) [(log (TG/HDL-C)], concentrations, and size of lipoproteins and changes in coronary artery stenosis in participants in the HDL-Atherosclerosis Treatment Study. A total of 160 patients was treated with simvastatin (S), niacin (N), antioxidants (A) and placebo (P) in four regimens. FER(HDL) was measured using a radioassay; the size and concentration of lipoprotein subclasses were determined by nuclear magnetic resonance spectroscopy. The S+N and S+N+A therapy decreased AIP and FER(HDL), reduced total VLDL (mostly the large and medium size particles), decreased total LDL particles (mostly the small size), and increased total HDL particles (mostly the large size). FER(HDL) and AIP correlated negatively with particle sizes of HDL and LDL, positively with VLDL particle size, and closely with each other (r = 0.729). Changes in the proportions of small and large lipoprotein particles, which were reflected by FER(HDL) and AIP, corresponded with findings on coronary angiography. Logistic regression analysis of the changes in the coronary stenosis showed that probability of progression was best explained by FER(HDL) (P = 0.005). FER(HDL) and AIP reflect the actual composition of the lipoprotein spectrum and thus predict both the cardiovascular risk and effectiveness of therapy. AIP is already available for use in clinical practice as it can be readily calculated from the routine lipid profile.
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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 | 0.000 | 0.001 |
| 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".