Determinants of plasma HDL concentrations and reverse cholesterol transport
Bibliographic record
Abstract
PURPOSE OF REVIEW: One of the major mechanisms whereby HDL particles are felt to protect against atherosclerosis is that of reverse cholesterol transport from atherosclerotic lesion macrophages to the liver, with subsequent excretion of cholesterol in the bile. This review focuses on recent progress in our understanding of reverse cholesterol transport and the factors that determine plasma HDL cholesterol concentrations. RECENT FINDINGS: The liver and intestine are the major sites of apolipoprotein A-I synthesis and nascent HDL particle secretion. The liver has recently been shown to be a major contributor to the plasma HDL-cholesterol concentration, but the precise site or mechanism whereby hepatically-synthesized HDL acquire the bulk of their lipid content remains to be determined. Contrastingly, macrophages contribute little to the plasma HDL cholesterol pool, whereas the quantitatively small macrophage-specific reverse cholesterol transport contributes disproportionately to protection against atherosclerosis. Studies have highlighted the coordinate action of cell surface lipid transporters, cholesterol esterification enzymes and lipid transfer factors in the early steps of reverse cholesterol transport and the recycling of pre-beta HDL particles to create a ready supply of cholesterol acceptor HDL particles. Most of the variation in plasma HDL-cholesterol levels in human populations is accounted for by variations in HDL clearance rather than production. SUMMARY: Our understanding of the in-vivo metabolism of HDL particles and their role in reverse cholesterol transport is rapidly evolving, with long-standing concepts being constantly challenged by emerging evidence. An in-depth understanding of HDL metabolism will guide the rational design of novel pharmacological therapies that effectively protect against 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".